diff --git a/ludwig/error.py b/ludwig/error.py
new file mode 100644
index 00000000000..2eb67f45320
--- /dev/null
+++ b/ludwig/error.py
@@ -0,0 +1,46 @@
+# Copyright (c) 2022 Predibase, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from ludwig.api_annotations import PublicAPI
+
+
+@PublicAPI
+class LudwigError(Exception):
+    """Base class for all custom exceptions raised by the Ludwig framework."""
+
+    pass
+
+
+@PublicAPI
+class InputDataError(LudwigError, ValueError):
+    """Exception raised for errors in the input data.
+
+    Appropriate for data which is not convertible to the input feature type, columns with all missing values,
+    categorical columns with only one category, etc...
+
+    Attributes:
+        column - The name of the input column which caused the error
+        feature_type - The Ludwig feature type which caused the error (number, binary, category...).
+        message - An error message describing the situation.
+    """
+
+    def __init__(self, column_name: str, feature_type: str, message: str):
+        self.column_name = column_name
+        self.feature_type = feature_type
+        self.message = message
+        super().__init__(message)
+
+    def __str__(self):
+        return f'Column "{self.column_name}" as {self.feature_type} feature: {self.message}'
diff --git a/ludwig/features/binary_feature.py b/ludwig/features/binary_feature.py
index 1caaa7c82f5..f4dc433e770 100644
--- a/ludwig/features/binary_feature.py
+++ b/ludwig/features/binary_feature.py
@@ -41,6 +41,7 @@
     TIED,
     TYPE,
 )
+from ludwig.error import InputDataError
 from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
 from ludwig.schema.features.binary_feature import BinaryInputFeatureConfig, BinaryOutputFeatureConfig
 from ludwig.utils import calibration, output_feature_utils, strings_utils
@@ -165,9 +166,8 @@ def get_feature_meta(column: DataFrame, preprocessing_parameters: Dict[str, Any]
 
         distinct_values = backend.df_engine.compute(column.drop_duplicates())
         if len(distinct_values) > 2:
-            raise ValueError(
-                f"Binary feature column {column.name} expects 2 distinct values, "
-                f"found: {distinct_values.values.tolist()}"
+            raise InputDataError(
+                column.name, BINARY, f"expects 2 distinct values, found {distinct_values.values.tolist()}"
             )
         if preprocessing_parameters["fallback_true_label"]:
             fallback_true_label = preprocessing_parameters["fallback_true_label"]
diff --git a/ludwig/features/category_feature.py b/ludwig/features/category_feature.py
index 66ac6e15383..76f378619fa 100644
--- a/ludwig/features/category_feature.py
+++ b/ludwig/features/category_feature.py
@@ -42,6 +42,7 @@
     TOP_K,
     TYPE,
 )
+from ludwig.error import InputDataError
 from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
 from ludwig.schema.features.category_feature import CategoryInputFeatureConfig, CategoryOutputFeatureConfig
 from ludwig.utils import calibration, output_feature_utils
@@ -136,8 +137,12 @@ def get_feature_meta(column, preprocessing_parameters, backend):
             num_most_frequent=preprocessing_parameters["most_common"],
             processor=backend.df_engine,
         )
-
-        return {"idx2str": idx2str, "str2idx": str2idx, "str2freq": str2freq, "vocab_size": len(str2idx)}
+        vocab_size = len(str2idx)
+        if vocab_size <= 1:
+            raise InputDataError(
+                column.name, CATEGORY, f"At least 2 distinct values are required, column only contains {str(idx2str)}"
+            )
+        return {"idx2str": idx2str, "str2idx": str2idx, "str2freq": str2freq, "vocab_size": vocab_size}
 
     @staticmethod
     def feature_data(column, metadata):