diff --git a/examples/decoding/linear_model_patterns.py b/examples/decoding/linear_model_patterns.py
index c1390cbb0d3..7373c0a18b3 100644
--- a/examples/decoding/linear_model_patterns.py
+++ b/examples/decoding/linear_model_patterns.py
@@ -79,7 +79,7 @@
 
 # Extract and plot spatial filters and spatial patterns
 for name, coef in (("patterns", model.patterns_), ("filters", model.filters_)):
-    # We fitted the linear model onto Z-scored data. To make the filters
+    # We fit the linear model on Z-scored data. To make the filters
     # interpretable, we must reverse this normalization step
     coef = scaler.inverse_transform([coef])[0]
 
diff --git a/mne/decoding/tests/test_transformer.py b/mne/decoding/tests/test_transformer.py
index bcb2bef58e9..a0e2a39c2e0 100644
--- a/mne/decoding/tests/test_transformer.py
+++ b/mne/decoding/tests/test_transformer.py
@@ -17,11 +17,14 @@
 
 from sklearn.decomposition import PCA
 from sklearn.kernel_ridge import KernelRidge
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
 from sklearn.utils.estimator_checks import parametrize_with_checks
 
-from mne import Epochs, create_info, io, pick_types, read_events
+from mne import Epochs, EpochsArray, create_info, io, pick_types, read_events
 from mne.decoding import (
     FilterEstimator,
+    LinearModel,
     PSDEstimator,
     Scaler,
     TemporalFilter,
@@ -218,9 +221,16 @@ def test_vectorizer():
     assert_equal(vect.fit_transform(data[1:]).shape, (149, 108))
 
     # check if raised errors are working correctly
-    vect.fit(np.random.rand(105, 12, 3))
-    pytest.raises(ValueError, vect.transform, np.random.rand(105, 12, 3, 1))
-    pytest.raises(ValueError, vect.inverse_transform, np.random.rand(102, 12, 12))
+    X = np.random.default_rng(0).standard_normal((105, 12, 3))
+    y = np.arange(X.shape[0]) % 2
+    pytest.raises(ValueError, vect.transform, X[..., np.newaxis])
+    pytest.raises(ValueError, vect.inverse_transform, X[:, :-1])
+
+    # And that pipelines work properly
+    X_arr = EpochsArray(X, create_info(12, 1000.0, "eeg"))
+    vect.fit(X_arr)
+    clf = make_pipeline(Vectorizer(), StandardScaler(), LinearModel())
+    clf.fit(X_arr, y)
 
 
 def test_unsupervised_spatial_filter():
diff --git a/mne/decoding/transformer.py b/mne/decoding/transformer.py
index fe8956c1dbe..56d36415d2d 100644
--- a/mne/decoding/transformer.py
+++ b/mne/decoding/transformer.py
@@ -15,6 +15,7 @@
     pick_info,
 )
 from ..cov import _check_scalings_user
+from ..epochs import BaseEpochs
 from ..filter import filter_data
 from ..time_frequency import psd_array_multitaper
 from ..utils import _check_option, _validate_type, fill_doc
@@ -34,6 +35,12 @@ def _check_data(
         multi_output=False,
         check_n_features=True,
     ):
+        # Sklearn calls asarray under the hood which works, but elsewhere they check for
+        # __len__ then look at the size of obj[0]... which is an epoch of shape (1, ...)
+        # rather than what they expect (shape (...)). So we explicitly get the NumPy
+        # array to make everyone happy.
+        if isinstance(epochs_data, BaseEpochs):
+            epochs_data = epochs_data.get_data(copy=False)
         kwargs = dict(dtype=np.float64, allow_nd=True, order="C", force_writeable=True)
         if hasattr(self, "n_features_in_") and check_n_features:
             if y is None: