diff --git a/src/skprometheus/preprocessing.py b/src/skprometheus/preprocessing.py
index 1dd7711..69f096e 100644
--- a/src/skprometheus/preprocessing.py
+++ b/src/skprometheus/preprocessing.py
@@ -1,3 +1,4 @@
+import numpy as np
 from functools import wraps
 
 from sklearn import preprocessing
@@ -5,6 +6,17 @@
 from skprometheus.utils import get_feature_names
 
 
+def feature_category_count(X, categories):
+
+    features = get_feature_names(X)
+
+    for idx, row in enumerate(categories.T):
+        for category in row:
+            if category is None:
+                category = "missing"
+            MetricRegistry.model_categorical(feature=str(features[idx]), category=str(category)).inc()
+
+
 class OneHotEncoder(preprocessing.OneHotEncoder):
     """
     OneHotEncoder that adds metrics to the prometheus metric registry.
@@ -24,15 +36,38 @@ def transform(self, X):
         metric registry.
         """
         transformed_X = super().transform(X)
-        features = get_feature_names(X)
 
         # Use inverse method on transformed_X to get all missing values back as 'None'
         categories = self.inverse_transform(transformed_X)
 
-        for idx, row in enumerate(categories.T):
-            for category in row:
-                if not category:
-                    category = "missing"
-                MetricRegistry.model_categorical(feature=str(features[idx]), category=str(category)).inc()
+        feature_category_count(X, categories)
+
+        return transformed_X
+
+
+class OrdinalEncoder(preprocessing.OrdinalEncoder):
+    """
+    OrdinalEncoder that adds metrics to the prometheus metric registry.
+    """
+    @wraps(preprocessing.OrdinalEncoder.__init__, assigned=["__signature__"])
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        MetricRegistry.add_counter(
+            "model_categorical",
+            "Counts category occurrence for each categorical feature.",
+            additional_labels=("feature", "category"),
+        )
+
+    def transform(self, X):
+        """
+        Transform method that adds the count for each category in each feature to the prometheus
+        metric registry.
+        """
+        transformed_X = super().transform(X)
+
+        # Use inverse method on transformed_X to get all missing values back as 'None'
+        categories = self.inverse_transform(transformed_X)
+
+        feature_category_count(X, categories)
 
         return transformed_X
diff --git a/src/skprometheus/utils.py b/src/skprometheus/utils.py
index 3f189bb..3f77616 100644
--- a/src/skprometheus/utils.py
+++ b/src/skprometheus/utils.py
@@ -32,5 +32,5 @@ def get_feature_names(X):
     if isinstance(X, pd.DataFrame):
         return X.columns
     else:
-        X = check_array(X, force_all_finite=False)
+        X = check_array(X, dtype = None, force_all_finite=False)
         return list(range(X.shape[1]))
diff --git a/tests/test_preprocessing.py b/tests/test_preprocessing.py
index 864b5d7..f1169b6 100644
--- a/tests/test_preprocessing.py
+++ b/tests/test_preprocessing.py
@@ -37,6 +37,7 @@ def test_OneHotEncoder():
 
     assert REGISTRY.get_sample_value('skprom_model_categorical_total', {'feature': '2', 'category': '4'}) == 2
     assert REGISTRY.get_sample_value('skprom_model_categorical_total', {'feature': '3', 'category': '9'}) == 1
+    assert REGISTRY.get_sample_value('skprom_model_categorical_total', {'feature': '3', 'category': '0'}) == 1
 
 
 def test_OneHotEncoder_pandas():