diff --git a/factor_analyzer/factor_analyzer.py b/factor_analyzer/factor_analyzer.py
index e1f6f67..524ea5e 100644
--- a/factor_analyzer/factor_analyzer.py
+++ b/factor_analyzer/factor_analyzer.py
@@ -625,6 +625,15 @@ def fit(self, X, y=None):
         else:
             X = X.copy()
 
+        n_features = X.shape[1]
+        if self.n_factors > n_features:
+            warnings. warn(
+                "n_factors ({0}) cannot exceed the number of features ({1});"
+                "the number of factors will be constrained to {1}.".format(
+                    self.n_factors, n_features
+                )
+            )
+
         # now check the array, and make sure it
         # meets all of our expected criteria
         X = check_array(X, ensure_all_finite="allow-nan", estimator=self, copy=True)
diff --git a/tests/test_factor_analyzer.py b/tests/test_factor_analyzer.py
index e8306a8..bbf212e 100644
--- a/tests/test_factor_analyzer.py
+++ b/tests/test_factor_analyzer.py
@@ -11,6 +11,7 @@
 
 import numpy as np
 import pandas as pd
+import pytest
 from numpy.testing import assert_array_almost_equal
 from pandas.testing import assert_frame_equal
 from sklearn.model_selection import GridSearchCV
@@ -249,3 +250,10 @@ def test_sufficiency(self):
             "tests/expected/test01/sufficiency_ml_none_15_test01.csv"
         )
         assert_frame_equal(df_computed, df_expected)
+
+
+def test_n_factors_exceeds_n_features_warns():
+    rng = np.random.default_rng(0)
+    X = rng.normal(size=(200, 6))
+    with pytest.warns(UserWarning):
+        FactorAnalyzer(n_factors=10, rotation=None).fit(X)