diff --git a/pl_examples/domain_templates/generative_adversarial_net.py b/pl_examples/domain_templates/generative_adversarial_net.py
index fd2cf69f14259..cef2107550bdb 100644
--- a/pl_examples/domain_templates/generative_adversarial_net.py
+++ b/pl_examples/domain_templates/generative_adversarial_net.py
@@ -206,7 +206,8 @@ def on_train_epoch_end(self):
         # log sampled images
         sample_imgs = self(z)
         grid = torchvision.utils.make_grid(sample_imgs)
-        self.logger.experiment.add_image("generated_images", grid, self.current_epoch)
+        for logger in self.loggers:
+            logger.experiment.add_image("generated_images", grid, self.current_epoch)
 
 
 def main(args: Namespace) -> None:
diff --git a/pytorch_lightning/callbacks/device_stats_monitor.py b/pytorch_lightning/callbacks/device_stats_monitor.py
index eaf67db9189e2..f9cb3cf623c1b 100644
--- a/pytorch_lightning/callbacks/device_stats_monitor.py
+++ b/pytorch_lightning/callbacks/device_stats_monitor.py
@@ -44,7 +44,7 @@ class DeviceStatsMonitor(Callback):
     """
 
     def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
-        if not trainer.logger:
+        if not trainer.loggers:
             raise MisconfigurationException("Cannot use DeviceStatsMonitor callback with Trainer that has no logger.")
 
     def on_train_batch_start(
@@ -55,7 +55,7 @@ def on_train_batch_start(
         batch_idx: int,
         unused: Optional[int] = 0,
     ) -> None:
-        if not trainer.logger:
+        if not trainer.loggers:
             raise MisconfigurationException("Cannot use `DeviceStatsMonitor` callback with `Trainer(logger=False)`.")
 
         if not trainer.logger_connector.should_update_logs:
@@ -63,9 +63,10 @@ def on_train_batch_start(
 
         device = trainer.strategy.root_device
         device_stats = trainer.accelerator.get_device_stats(device)
-        separator = trainer.logger.group_separator
-        prefixed_device_stats = _prefix_metric_keys(device_stats, "on_train_batch_start", separator)
-        trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
+        for logger in trainer.loggers:
+            separator = logger.group_separator
+            prefixed_device_stats = _prefix_metric_keys(device_stats, "on_train_batch_start", separator)
+            logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
 
     def on_train_batch_end(
         self,
@@ -76,7 +77,7 @@ def on_train_batch_end(
         batch_idx: int,
         unused: Optional[int] = 0,
     ) -> None:
-        if not trainer.logger:
+        if not trainer.loggers:
             raise MisconfigurationException("Cannot use `DeviceStatsMonitor` callback with `Trainer(logger=False)`.")
 
         if not trainer.logger_connector.should_update_logs:
@@ -84,9 +85,10 @@ def on_train_batch_end(
 
         device = trainer.strategy.root_device
         device_stats = trainer.accelerator.get_device_stats(device)
-        separator = trainer.logger.group_separator
-        prefixed_device_stats = _prefix_metric_keys(device_stats, "on_train_batch_end", separator)
-        trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
+        for logger in trainer.loggers:
+            separator = logger.group_separator
+            prefixed_device_stats = _prefix_metric_keys(device_stats, "on_train_batch_end", separator)
+            logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
 
 
 def _prefix_metric_keys(metrics_dict: Dict[str, float], prefix: str, separator: str) -> Dict[str, float]:
diff --git a/pytorch_lightning/callbacks/gpu_stats_monitor.py b/pytorch_lightning/callbacks/gpu_stats_monitor.py
index a871bfa309c96..2c2949e53bf7e 100644
--- a/pytorch_lightning/callbacks/gpu_stats_monitor.py
+++ b/pytorch_lightning/callbacks/gpu_stats_monitor.py
@@ -123,7 +123,7 @@ def __init__(
         self._gpu_ids: List[str] = []  # will be assigned later in setup()
 
     def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
-        if not trainer.logger:
+        if not trainer.loggers:
             raise MisconfigurationException("Cannot use GPUStatsMonitor callback with Trainer that has no logger.")
 
         if trainer.strategy.root_device.type != "cuda":
@@ -161,8 +161,8 @@ def on_train_batch_start(
             # First log at beginning of second step
             logs["batch_time/inter_step (ms)"] = (time.time() - self._snap_inter_step_time) * 1000
 
-        assert trainer.logger is not None
-        trainer.logger.log_metrics(logs, step=trainer.global_step)
+        for logger in trainer.loggers:
+            logger.log_metrics(logs, step=trainer.global_step)
 
     @rank_zero_only
     def on_train_batch_end(
@@ -186,8 +186,8 @@ def on_train_batch_end(
         if self._log_stats.intra_step_time and self._snap_intra_step_time:
             logs["batch_time/intra_step (ms)"] = (time.time() - self._snap_intra_step_time) * 1000
 
-        assert trainer.logger is not None
-        trainer.logger.log_metrics(logs, step=trainer.global_step)
+        for logger in trainer.loggers:
+            logger.log_metrics(logs, step=trainer.global_step)
 
     @staticmethod
     def _get_gpu_ids(device_ids: List[int]) -> List[str]:
diff --git a/pytorch_lightning/callbacks/lr_monitor.py b/pytorch_lightning/callbacks/lr_monitor.py
index 0f3519d8fe8c2..00ff007af5e41 100644
--- a/pytorch_lightning/callbacks/lr_monitor.py
+++ b/pytorch_lightning/callbacks/lr_monitor.py
@@ -104,7 +104,7 @@ def on_train_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> No
             MisconfigurationException:
                 If ``Trainer`` has no ``logger``.
         """
-        if not trainer.logger:
+        if not trainer.loggers:
             raise MisconfigurationException(
                 "Cannot use `LearningRateMonitor` callback with `Trainer` that has no logger."
             )
@@ -149,7 +149,6 @@ def _check_no_key(key: str) -> bool:
         self.last_momentum_values = {name + "-momentum": None for name in names_flatten}
 
     def on_train_batch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None:
-        assert trainer.logger is not None
         if not trainer.logger_connector.should_update_logs:
             return
 
@@ -158,16 +157,17 @@ def on_train_batch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any)
             latest_stat = self._extract_stats(trainer, interval)
 
             if latest_stat:
-                trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
+                for logger in trainer.loggers:
+                    logger.log_metrics(latest_stat, step=trainer.global_step)
 
     def on_train_epoch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None:
-        assert trainer.logger is not None
         if self.logging_interval != "step":
             interval = "epoch" if self.logging_interval is None else "any"
             latest_stat = self._extract_stats(trainer, interval)
 
             if latest_stat:
-                trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
+                for logger in trainer.loggers:
+                    logger.log_metrics(latest_stat, step=trainer.global_step)
 
     def _extract_stats(self, trainer: "pl.Trainer", interval: str) -> Dict[str, float]:
         latest_stat = {}
diff --git a/pytorch_lightning/callbacks/model_checkpoint.py b/pytorch_lightning/callbacks/model_checkpoint.py
index 13791a70f97da..1e231abab2661 100644
--- a/pytorch_lightning/callbacks/model_checkpoint.py
+++ b/pytorch_lightning/callbacks/model_checkpoint.py
@@ -35,6 +35,7 @@
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.logger import _name, _version
 from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.types import _METRIC, _PATH, STEP_OUTPUT
 from pytorch_lightning.utilities.warnings import WarningCache
@@ -379,8 +380,9 @@ def save_checkpoint(self, trainer: "pl.Trainer") -> None:
         self._save_last_checkpoint(trainer, monitor_candidates)
 
         # notify loggers
-        if trainer.is_global_zero and trainer.logger:
-            trainer.logger.after_save_checkpoint(proxy(self))
+        if trainer.is_global_zero:
+            for logger in trainer.loggers:
+                logger.after_save_checkpoint(proxy(self))
 
     def _should_skip_saving_checkpoint(self, trainer: "pl.Trainer") -> bool:
         from pytorch_lightning.trainer.states import TrainerFn
@@ -572,20 +574,20 @@ def __resolve_ckpt_dir(self, trainer: "pl.Trainer") -> None:
         """
         if self.dirpath is not None:
             return  # short circuit
-
-        if trainer.logger is not None:
+        if trainer.loggers:
             if trainer.weights_save_path != trainer.default_root_dir:
                 # the user has changed weights_save_path, it overrides anything
                 save_dir = trainer.weights_save_path
-            else:
+            elif len(trainer.loggers) == 1:
                 save_dir = trainer.logger.save_dir or trainer.default_root_dir
+            else:
+                save_dir = trainer.default_root_dir
 
-            version = (
-                trainer.logger.version
-                if isinstance(trainer.logger.version, str)
-                else f"version_{trainer.logger.version}"
-            )
-            ckpt_path = os.path.join(save_dir, str(trainer.logger.name), version, "checkpoints")
+            name = _name(trainer.loggers)
+            version = _version(trainer.loggers)
+            version = version if isinstance(version, str) else f"version_{version}"
+
+            ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints")
         else:
             ckpt_path = os.path.join(trainer.weights_save_path, "checkpoints")
 
diff --git a/pytorch_lightning/callbacks/progress/base.py b/pytorch_lightning/callbacks/progress/base.py
index 291fb495a81c9..3ee1f83a547c5 100644
--- a/pytorch_lightning/callbacks/progress/base.py
+++ b/pytorch_lightning/callbacks/progress/base.py
@@ -15,6 +15,7 @@
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks import Callback
+from pytorch_lightning.utilities.logger import _version
 from pytorch_lightning.utilities.rank_zero import rank_zero_warn
 
 
@@ -213,11 +214,12 @@ def get_standard_metrics(trainer: "pl.Trainer", pl_module: "pl.LightningModule")
     if pl_module.truncated_bptt_steps > 0:
         items_dict["split_idx"] = trainer.fit_loop.split_idx
 
-    if trainer.logger is not None and trainer.logger.version is not None:
-        version = trainer.logger.version
-        if isinstance(version, str):
-            # show last 4 places of long version strings
-            version = version[-4:]
-        items_dict["v_num"] = version
+    if trainer.loggers:
+        version = _version(trainer.loggers)
+        if version is not None:
+            if isinstance(version, str):
+                # show last 4 places of long version strings
+                version = version[-4:]
+            items_dict["v_num"] = version
 
     return items_dict
diff --git a/pytorch_lightning/callbacks/xla_stats_monitor.py b/pytorch_lightning/callbacks/xla_stats_monitor.py
index cfbffaff41f47..ebc6ca9d72357 100644
--- a/pytorch_lightning/callbacks/xla_stats_monitor.py
+++ b/pytorch_lightning/callbacks/xla_stats_monitor.py
@@ -70,7 +70,7 @@ def __init__(self, verbose: bool = True) -> None:
         self._verbose = verbose
 
     def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        if not trainer.logger:
+        if not trainer.loggers:
             raise MisconfigurationException("Cannot use XLAStatsMonitor callback with Trainer that has no logger.")
 
         if isinstance(trainer.accelerator, TPUAccelerator):
@@ -88,7 +88,7 @@ def on_train_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningMo
         self._start_time = time.time()
 
     def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        if not trainer.logger:
+        if not trainer.loggers:
             raise MisconfigurationException("Cannot use XLAStatsMonitor callback with Trainer that has no logger.")
 
         device = trainer.strategy.root_device
@@ -102,10 +102,11 @@ def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModu
         peak_memory = trainer.strategy.reduce(peak_memory) * 0.001
         epoch_time = trainer.strategy.reduce(epoch_time)
 
-        trainer.logger.log_metrics(
-            {"avg. free memory (MB)": float(free_memory), "avg. peak memory (MB)": float(peak_memory)},
-            step=trainer.current_epoch,
-        )
+        for logger in trainer.loggers:
+            logger.log_metrics(
+                {"avg. free memory (MB)": float(free_memory), "avg. peak memory (MB)": float(peak_memory)},
+                step=trainer.current_epoch,
+            )
 
         if self._verbose:
             rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 57278a8af65aa..65c0e6dc978ad 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -253,7 +253,7 @@ def logger(self) -> Optional[LightningLoggerBase]:
 
     @property
     def loggers(self) -> List[LightningLoggerBase]:
-        """Reference to the loggers object in the Trainer."""
+        """Reference to the list of loggers in the Trainer."""
         return self.trainer.loggers if self.trainer else []
 
     def _apply_batch_transfer_handler(
diff --git a/pytorch_lightning/loops/epoch/training_epoch_loop.py b/pytorch_lightning/loops/epoch/training_epoch_loop.py
index c8eefedd3c327..87216944893ef 100644
--- a/pytorch_lightning/loops/epoch/training_epoch_loop.py
+++ b/pytorch_lightning/loops/epoch/training_epoch_loop.py
@@ -504,8 +504,10 @@ def _save_loggers_on_train_batch_end(self) -> None:
         """Flushes loggers to disk."""
         # when loggers should save to disk
         should_flush_logs = self.trainer.logger_connector.should_flush_logs
-        if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None:
-            self.trainer.logger.save()
+        # TODO: is_global_zero check should be moved to logger.save() implementation
+        if should_flush_logs and self.trainer.is_global_zero:
+            for logger in self.trainer.loggers:
+                logger.save()
 
     def _reload_dataloader_state_dict(self, data_fetcher: AbstractDataFetcher) -> None:
         if self._dataloader_state_dict:
diff --git a/pytorch_lightning/plugins/precision/precision_plugin.py b/pytorch_lightning/plugins/precision/precision_plugin.py
index c876dd5c909d2..5a5606c5f2576 100644
--- a/pytorch_lightning/plugins/precision/precision_plugin.py
+++ b/pytorch_lightning/plugins/precision/precision_plugin.py
@@ -155,7 +155,11 @@ def optimizer_step(
     def _track_grad_norm(self, trainer: "pl.Trainer") -> None:
         if trainer.track_grad_norm == -1:
             return
-        kwargs = {"group_separator": trainer.logger.group_separator} if trainer.logger is not None else {}
+
+        kwargs = {}
+        if len(trainer.loggers) == 1:
+            kwargs["group_separator"] = trainer.loggers[0].group_separator
+
         grad_norm_dict = grad_norm(trainer.lightning_module, trainer.track_grad_norm, **kwargs)
         if grad_norm_dict:
             prev_fx = trainer.lightning_module._current_fx_name
diff --git a/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py b/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py
index 2e241e9845ef8..428713ff3347e 100644
--- a/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py
+++ b/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py
@@ -17,7 +17,7 @@
 
 import pytorch_lightning as pl
 from pytorch_lightning.accelerators import GPUAccelerator
-from pytorch_lightning.loggers import LightningLoggerBase, LoggerCollection, TensorBoardLogger
+from pytorch_lightning.loggers import LightningLoggerBase, TensorBoardLogger
 from pytorch_lightning.plugins.environments.slurm_environment import SLURMEnvironment
 from pytorch_lightning.trainer.connectors.logger_connector.result import _METRICS, _OUT_DICT, _PBAR_DICT
 from pytorch_lightning.trainer.states import RunningStage
@@ -90,15 +90,15 @@ def should_update_logs(self) -> bool:
     def configure_logger(self, logger: Union[bool, LightningLoggerBase, Iterable[LightningLoggerBase]]) -> None:
         if isinstance(logger, bool):
             # default logger
-            self.trainer.logger = (
-                TensorBoardLogger(save_dir=self.trainer.default_root_dir, version=SLURMEnvironment.job_id())
+            self.trainer.loggers = (
+                [TensorBoardLogger(save_dir=self.trainer.default_root_dir, version=SLURMEnvironment.job_id())]
                 if logger
-                else None
+                else []
             )
         elif isinstance(logger, Iterable):
-            self.trainer.logger = LoggerCollection(logger)
+            self.trainer.loggers = list(logger)
         else:
-            self.trainer.logger = logger
+            self.trainer.loggers = [logger]
 
     def log_metrics(self, metrics: _OUT_DICT, step: Optional[int] = None) -> None:
         """Logs the metric dict passed in. If `step` parameter is None and `step` key is presented is metrics, uses
@@ -109,7 +109,7 @@ def log_metrics(self, metrics: _OUT_DICT, step: Optional[int] = None) -> None:
             step: Step for which metrics should be logged. Default value is `self.global_step` during training or
                 the total validation / test log step count during validation and testing.
         """
-        if self.trainer.logger is None or not metrics:
+        if not self.trainer.loggers or not metrics:
             return
 
         self._logged_metrics.update(metrics)
@@ -126,11 +126,12 @@ def log_metrics(self, metrics: _OUT_DICT, step: Optional[int] = None) -> None:
             step = self.trainer.global_step
 
         # log actual metrics
-        if self._override_agg_and_log_metrics:
-            self.trainer.logger.agg_and_log_metrics(metrics=scalar_metrics, step=step)
-        else:
-            self.trainer.logger.log_metrics(metrics=scalar_metrics, step=step)
-        self.trainer.logger.save()
+        for logger in self.trainer.loggers:
+            if self._override_agg_and_log_metrics:
+                logger.agg_and_log_metrics(metrics=scalar_metrics, step=step)
+            else:
+                logger.log_metrics(metrics=scalar_metrics, step=step)
+            logger.save()
 
     """
     Evaluation metric updates
diff --git a/pytorch_lightning/trainer/connectors/signal_connector.py b/pytorch_lightning/trainer/connectors/signal_connector.py
index ee32818691a92..8d8ac428fdde7 100644
--- a/pytorch_lightning/trainer/connectors/signal_connector.py
+++ b/pytorch_lightning/trainer/connectors/signal_connector.py
@@ -66,8 +66,8 @@ def slurm_sigusr1_handler_fn(self, signum: _SIGNUM, frame: FrameType) -> None:
         rank_zero_info("handling SIGUSR1")
 
         # save logger to make sure we get all the metrics
-        if self.trainer.logger:
-            self.trainer.logger.finalize("finished")
+        for logger in self.trainer.loggers:
+            logger.finalize("finished")
         hpc_save_path = self.trainer._checkpoint_connector.hpc_save_path(self.trainer.weights_save_path)
         self.trainer.save_checkpoint(hpc_save_path)
 
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index 64988e3a580e1..a26c2babc40fb 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -657,7 +657,7 @@ def _init_debugging_flags(
             self.fit_loop.max_epochs = 1
             val_check_interval = 1.0
             self.check_val_every_n_epoch = 1
-            self.logger = DummyLogger() if self.logger is not None else None
+            self.loggers = [DummyLogger()] if self.loggers else []
 
             rank_zero_info(
                 "Running in fast_dev_run mode: will run a full train,"
@@ -1246,41 +1246,43 @@ def _run(
         return results
 
     def _log_hyperparams(self) -> None:
+        if not self.loggers:
+            return
         # log hyper-parameters
         hparams_initial = None
 
-        if self.logger is not None:
-            # save exp to get started (this is where the first experiment logs are written)
-            datamodule_log_hyperparams = self.datamodule._log_hyperparams if self.datamodule is not None else False
-
-            if self.lightning_module._log_hyperparams and datamodule_log_hyperparams:
-                datamodule_hparams = self.datamodule.hparams_initial
-                lightning_hparams = self.lightning_module.hparams_initial
-                inconsistent_keys = []
-                for key in lightning_hparams.keys() & datamodule_hparams.keys():
-                    lm_val, dm_val = lightning_hparams[key], datamodule_hparams[key]
-                    if type(lm_val) != type(dm_val):
-                        inconsistent_keys.append(key)
-                    elif isinstance(lm_val, torch.Tensor) and id(lm_val) != id(dm_val):
-                        inconsistent_keys.append(key)
-                    elif lm_val != dm_val:
-                        inconsistent_keys.append(key)
-                if inconsistent_keys:
-                    raise MisconfigurationException(
-                        f"Error while merging hparams: the keys {inconsistent_keys} are present "
-                        "in both the LightningModule's and LightningDataModule's hparams "
-                        "but have different values."
-                    )
-                hparams_initial = {**lightning_hparams, **datamodule_hparams}
-            elif self.lightning_module._log_hyperparams:
-                hparams_initial = self.lightning_module.hparams_initial
-            elif datamodule_log_hyperparams:
-                hparams_initial = self.datamodule.hparams_initial
+        # save exp to get started (this is where the first experiment logs are written)
+        datamodule_log_hyperparams = self.datamodule._log_hyperparams if self.datamodule is not None else False
+
+        if self.lightning_module._log_hyperparams and datamodule_log_hyperparams:
+            datamodule_hparams = self.datamodule.hparams_initial
+            lightning_hparams = self.lightning_module.hparams_initial
+            inconsistent_keys = []
+            for key in lightning_hparams.keys() & datamodule_hparams.keys():
+                lm_val, dm_val = lightning_hparams[key], datamodule_hparams[key]
+                if type(lm_val) != type(dm_val):
+                    inconsistent_keys.append(key)
+                elif isinstance(lm_val, torch.Tensor) and id(lm_val) != id(dm_val):
+                    inconsistent_keys.append(key)
+                elif lm_val != dm_val:
+                    inconsistent_keys.append(key)
+            if inconsistent_keys:
+                raise MisconfigurationException(
+                    f"Error while merging hparams: the keys {inconsistent_keys} are present "
+                    "in both the LightningModule's and LightningDataModule's hparams "
+                    "but have different values."
+                )
+            hparams_initial = {**lightning_hparams, **datamodule_hparams}
+        elif self.lightning_module._log_hyperparams:
+            hparams_initial = self.lightning_module.hparams_initial
+        elif datamodule_log_hyperparams:
+            hparams_initial = self.datamodule.hparams_initial
 
+        for logger in self.loggers:
             if hparams_initial is not None:
-                self.logger.log_hyperparams(hparams_initial)
-            self.logger.log_graph(self.lightning_module)
-            self.logger.save()
+                logger.log_hyperparams(hparams_initial)
+            logger.log_graph(self.lightning_module)
+            logger.save()
 
     def _teardown(self):
         """This is the Trainer's internal teardown, unrelated to the `teardown` hooks in LightningModule and
@@ -1519,8 +1521,8 @@ def _call_teardown_hook(self) -> None:
 
         # todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
         # It might be related to xla tensors blocked when moving the cpu kill loggers.
-        if self.logger is not None:
-            self.logger.finalize("success")
+        for logger in self.loggers:
+            logger.finalize("success")
 
         # summarize profile results
         self.profiler.describe()
@@ -1890,7 +1892,7 @@ def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -
                 self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
                 self.val_check_batch = max(1, self.val_check_batch)
 
-        if self.logger and self.num_training_batches < self.log_every_n_steps:
+        if self.loggers and self.num_training_batches < self.log_every_n_steps:
             rank_zero_warn(
                 f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
                 f" Trainer(log_every_n_steps={self.log_every_n_steps}). Set a lower value for log_every_n_steps if"
@@ -2137,14 +2139,13 @@ def model(self, model: torch.nn.Module) -> None:
 
     @property
     def log_dir(self) -> Optional[str]:
-        if self.logger is None:
-            dirpath = self.default_root_dir
-        elif isinstance(self.logger, TensorBoardLogger):
-            dirpath = self.logger.log_dir
-        elif isinstance(self.logger, LoggerCollection):
-            dirpath = self.default_root_dir
+        if len(self.loggers) == 1:
+            if isinstance(self.logger, TensorBoardLogger):
+                dirpath = self.logger.log_dir
+            else:
+                dirpath = self.logger.save_dir
         else:
-            dirpath = self.logger.save_dir
+            dirpath = self.default_root_dir
 
         dirpath = self.strategy.broadcast(dirpath)
         return dirpath
diff --git a/pytorch_lightning/tuner/batch_size_scaling.py b/pytorch_lightning/tuner/batch_size_scaling.py
index 1526e570dabe5..3d5916e3f8bd9 100644
--- a/pytorch_lightning/tuner/batch_size_scaling.py
+++ b/pytorch_lightning/tuner/batch_size_scaling.py
@@ -109,7 +109,7 @@ def __scale_batch_reset_params(trainer: "pl.Trainer", steps_per_trial: int) -> N
     trainer.auto_scale_batch_size = None  # prevent recursion
     trainer.auto_lr_find = False  # avoid lr find being called multiple times
     trainer.fit_loop.max_steps = steps_per_trial  # take few steps
-    trainer.logger = DummyLogger() if trainer.logger is not None else None
+    trainer.loggers = [DummyLogger()] if trainer.loggers else []
     trainer.callbacks = []  # not needed before full run
     trainer.limit_train_batches = 1.0
 
diff --git a/pytorch_lightning/tuner/lr_finder.py b/pytorch_lightning/tuner/lr_finder.py
index 876ff7823b2dc..d929bbe2f87c7 100644
--- a/pytorch_lightning/tuner/lr_finder.py
+++ b/pytorch_lightning/tuner/lr_finder.py
@@ -267,7 +267,7 @@ def __lr_finder_reset_params(trainer: "pl.Trainer", num_training: int, early_sto
     # Use special lr logger callback
     trainer.callbacks = [_LRCallback(num_training, early_stop_threshold, progress_bar_refresh_rate=1)]
     # No logging
-    trainer.logger = DummyLogger() if trainer.logger is not None else None
+    trainer.loggers = [DummyLogger()] if trainer.loggers else []
     # Max step set to number of iterations
     trainer.fit_loop.max_steps = num_training
 
diff --git a/pytorch_lightning/utilities/logger.py b/pytorch_lightning/utilities/logger.py
index a66582fd8466f..ef27761a2ec6e 100644
--- a/pytorch_lightning/utilities/logger.py
+++ b/pytorch_lightning/utilities/logger.py
@@ -146,3 +146,19 @@ def _add_prefix(metrics: Dict[str, float], prefix: str, separator: str) -> Dict[
         metrics = {f"{prefix}{separator}{k}": v for k, v in metrics.items()}
 
     return metrics
+
+
+def _name(loggers: List[Any], separator: str = "_") -> str:
+    if len(loggers) == 1:
+        return loggers[0].name
+    else:
+        # Concatenate names together, removing duplicates and preserving order
+        return separator.join(dict.fromkeys(str(logger.name) for logger in loggers))
+
+
+def _version(loggers: List[Any], separator: str = "_") -> Union[int, str]:
+    if len(loggers) == 1:
+        return loggers[0].version
+    else:
+        # Concatenate versions together, removing duplicates and preserving order
+        return separator.join(dict.fromkeys(str(logger.version) for logger in loggers))
diff --git a/tests/checkpointing/test_model_checkpoint.py b/tests/checkpointing/test_model_checkpoint.py
index a5c3aae5b14d7..2c65426534aff 100644
--- a/tests/checkpointing/test_model_checkpoint.py
+++ b/tests/checkpointing/test_model_checkpoint.py
@@ -1273,7 +1273,7 @@ def test_none_monitor_saves_correct_best_model_path(tmpdir):
 def test_last_global_step_saved():
     # this should not save anything
     model_checkpoint = ModelCheckpoint(save_top_k=0, save_last=False, monitor="foo")
-    trainer = Mock()
+    trainer = MagicMock()
     trainer.callback_metrics = {"foo": 123}
     model_checkpoint.save_checkpoint(trainer)
     assert model_checkpoint._last_global_step_saved == -1
diff --git a/tests/loggers/test_base.py b/tests/loggers/test_base.py
index afcb811a795b6..cd7eec14eec77 100644
--- a/tests/loggers/test_base.py
+++ b/tests/loggers/test_base.py
@@ -186,10 +186,11 @@ def test_multiple_loggers_pickle(tmpdir):
     trainer = Trainer(logger=[logger1, logger2])
     pkl_bytes = pickle.dumps(trainer)
     trainer2 = pickle.loads(pkl_bytes)
-    trainer2.logger.log_metrics({"acc": 1.0}, 0)
+    for logger in trainer2.loggers:
+        logger.log_metrics({"acc": 1.0}, 0)
 
-    assert trainer2.logger[0].metrics_logged == {"acc": 1.0}
-    assert trainer2.logger[1].metrics_logged == {"acc": 1.0}
+    for logger in trainer2.loggers:
+        assert logger.metrics_logged == {"acc": 1.0}
 
 
 def test_adding_step_key(tmpdir):
diff --git a/tests/profiler/test_profiler.py b/tests/profiler/test_profiler.py
index f63a31f6d8aca..161c9e6e35670 100644
--- a/tests/profiler/test_profiler.py
+++ b/tests/profiler/test_profiler.py
@@ -24,8 +24,7 @@
 
 from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.callbacks import EarlyStopping, StochasticWeightAveraging
-from pytorch_lightning.loggers.base import DummyLogger, LoggerCollection
-from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
+from pytorch_lightning.loggers import CSVLogger, LoggerCollection, TensorBoardLogger
 from pytorch_lightning.profiler import AdvancedProfiler, PassThroughProfiler, PyTorchProfiler, SimpleProfiler
 from pytorch_lightning.profiler.pytorch import RegisterRecordFunction, warning_cache
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -467,7 +466,7 @@ def look_for_trace(trace_dir):
 
     model = BoringModel()
     # Wrap the logger in a list so it becomes a LoggerCollection
-    logger = [TensorBoardLogger(save_dir=tmpdir), DummyLogger()]
+    logger = [TensorBoardLogger(save_dir=tmpdir), CSVLogger(tmpdir)]
     trainer = Trainer(default_root_dir=tmpdir, profiler="pytorch", logger=logger, limit_train_batches=5, max_epochs=1)
     assert isinstance(trainer.logger, LoggerCollection)
     trainer.fit(model)
diff --git a/tests/trainer/properties/test_log_dir.py b/tests/trainer/properties/test_log_dir.py
index 71920a6b079bf..6777ec8183737 100644
--- a/tests/trainer/properties/test_log_dir.py
+++ b/tests/trainer/properties/test_log_dir.py
@@ -15,8 +15,7 @@
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.loggers import LoggerCollection, TensorBoardLogger
-from pytorch_lightning.loggers.base import DummyLogger
+from pytorch_lightning.loggers import CSVLogger, LoggerCollection, TensorBoardLogger
 from tests.helpers.boring_model import BoringModel
 
 
@@ -118,7 +117,7 @@ def test_logdir_logger_collection(tmpdir):
     trainer = Trainer(
         default_root_dir=default_root_dir,
         max_steps=2,
-        logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs"), DummyLogger()],
+        logger=[TensorBoardLogger(save_dir=save_dir, name="custom_logs"), CSVLogger(tmpdir)],
     )
     assert isinstance(trainer.logger, LoggerCollection)
     assert trainer.log_dir == default_root_dir
diff --git a/tests/trainer/properties/test_loggers.py b/tests/trainer/properties/test_loggers.py
index 606c7b641ae1d..d3db78986f361 100644
--- a/tests/trainer/properties/test_loggers.py
+++ b/tests/trainer/properties/test_loggers.py
@@ -30,18 +30,20 @@ def test_trainer_loggers_property():
     # trainer.loggers should create a list of size 1
     trainer = Trainer(logger=logger1)
 
+    assert trainer.logger == logger1
     assert trainer.loggers == [logger1]
 
     # trainer.loggers should be an empty list
     trainer = Trainer(logger=False)
 
+    assert trainer.logger is None
     assert trainer.loggers == []
 
     # trainer.loggers should be a list of size 1 holding the default logger
     trainer = Trainer(logger=True)
 
     assert trainer.loggers == [trainer.logger]
-    assert type(trainer.loggers[0]) == TensorBoardLogger
+    assert isinstance(trainer.logger, TensorBoardLogger)
 
 
 def test_trainer_loggers_setters():
diff --git a/tests/utilities/test_logger.py b/tests/utilities/test_logger.py
index 8d9b495fb96bf..6b67272289fc3 100644
--- a/tests/utilities/test_logger.py
+++ b/tests/utilities/test_logger.py
@@ -17,12 +17,15 @@
 import torch
 
 from pytorch_lightning import Trainer
+from pytorch_lightning.loggers import CSVLogger
 from pytorch_lightning.utilities.logger import (
     _add_prefix,
     _convert_params,
     _flatten_dict,
+    _name,
     _sanitize_callable_params,
     _sanitize_params,
+    _version,
 )
 
 
@@ -172,3 +175,37 @@ def test_add_prefix():
     assert "prefix-metric2" not in metrics
     assert metrics["prefix2_prefix-metric1"] == 1
     assert metrics["prefix2_prefix-metric2"] == 2
+
+
+def test_name(tmpdir):
+    """Verify names of loggers are concatenated properly."""
+    logger1 = CSVLogger(tmpdir, name="foo")
+    logger2 = CSVLogger(tmpdir, name="bar")
+    logger3 = CSVLogger(tmpdir, name="foo")
+    logger4 = CSVLogger(tmpdir, name="baz")
+    loggers = [logger1, logger2, logger3, logger4]
+    name = _name([])
+    assert name == ""
+    name = _name([logger3])
+    assert name == "foo"
+    name = _name(loggers)
+    assert name == "foo_bar_baz"
+    name = _name(loggers, "-")
+    assert name == "foo-bar-baz"
+
+
+def test_version(tmpdir):
+    """Verify versions of loggers are concatenated properly."""
+    logger1 = CSVLogger(tmpdir, version=0)
+    logger2 = CSVLogger(tmpdir, version=2)
+    logger3 = CSVLogger(tmpdir, version=1)
+    logger4 = CSVLogger(tmpdir, version=0)
+    loggers = [logger1, logger2, logger3, logger4]
+    version = _version([])
+    assert version == ""
+    version = _version([logger3])
+    assert version == 1
+    version = _version(loggers)
+    assert version == "0_2_1"
+    version = _version(loggers, "-")
+    assert version == "0-2-1"