diff --git a/.gitignore b/.gitignore
index 4328f217e..6d07f4bc3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -22,6 +22,8 @@ var/
 *.egg-info/
 .installed.cfg
 *.egg
+Pipfile
+Pipfile.lock
 
 # PyInstaller
 #  Usually these files are written by a python script from a template
diff --git a/imblearn/utils/_validation.py b/imblearn/utils/_validation.py
index 8cb505f50..d1b0069b7 100644
--- a/imblearn/utils/_validation.py
+++ b/imblearn/utils/_validation.py
@@ -12,6 +12,7 @@
 from sklearn.base import clone
 from sklearn.neighbors._base import KNeighborsMixin
 from sklearn.neighbors import NearestNeighbors
+from sklearn.utils import column_or_1d
 from sklearn.utils.multiclass import type_of_target
 
 from ..exceptions import raise_isinstance_error
@@ -96,6 +97,8 @@ def check_target_type(y, indicate_one_vs_all=False):
                 "multioutput targets are not supported."
             )
         y = y.argmax(axis=1)
+    else:
+        y = column_or_1d(y)
 
     return (y, type_y == "multilabel-indicator") if indicate_one_vs_all else y
 
diff --git a/imblearn/utils/estimator_checks.py b/imblearn/utils/estimator_checks.py
index 43f117ba3..51a039f85 100644
--- a/imblearn/utils/estimator_checks.py
+++ b/imblearn/utils/estimator_checks.py
@@ -44,6 +44,7 @@ def _yield_sampler_checks(name, Estimator):
     yield check_samplers_multiclass_ova
     yield check_samplers_preserve_dtype
     yield check_samplers_sample_indices
+    yield check_samplers_2d_target
 
 
 def _yield_classifier_checks(name, Estimator):
@@ -283,6 +284,20 @@ def check_samplers_multiclass_ova(name, Sampler):
     assert_allclose(y_res, y_res_ova.argmax(axis=1))
 
 
+def check_samplers_2d_target(name, Sampler):
+    X, y = make_classification(
+        n_samples=100,
+        n_classes=3,
+        n_informative=4,
+        weights=[0.2, 0.3, 0.5],
+        random_state=0,
+    )
+
+    y = y.reshape(-1, 1)  # Make the target 2d
+    sampler = Sampler()
+    sampler.fit_resample(X, y)
+
+
 def check_samplers_preserve_dtype(name, Sampler):
     X, y = make_classification(
         n_samples=1000,