diff --git a/ctapipe/image/cleaning.py b/ctapipe/image/cleaning.py
index 62c199973f3..0fa73f8e29c 100644
--- a/ctapipe/image/cleaning.py
+++ b/ctapipe/image/cleaning.py
@@ -133,9 +133,9 @@ def time_clustering(
     firstly applied. The cut is performed relative to the noise to account for, e.g., bright stars. Alternatively,
     a hard cut could also be performed.
 
-    DBSCAN runs with the reconstructed times and pixel positions after rescaling. The reconstructed times and
-    pixel positions are rescaled because eps is not dimension dependent. If scaling is performed properly, eps
-    can be set to 1. DBSCAN returns the cluster IDs of each point being -1 the label for noise pixels.
+    DBSCAN runs with the reconstructed times and pixel positions after scaling. Scaling is needed because eps
+    is not dimension dependent. If scaling is performed properly, eps can be set to 1. DBSCAN returns the
+    cluster IDs of each point being -1 the label for noise pixels.
 
     Parameters
     ----------
diff --git a/ctapipe/image/tests/test_cleaning.py b/ctapipe/image/tests/test_cleaning.py
index b6e98cf95a4..92b03f1416c 100644
--- a/ctapipe/image/tests/test_cleaning.py
+++ b/ctapipe/image/tests/test_cleaning.py
@@ -343,7 +343,7 @@ def test_time_cleaning():
     core_neighbors = geom.neighbors[core_pixel]
 
     charge[core_pixel], charge[core_neighbors] = 15, 5
-    peak_time[core_pixel], peak_time[core_neighbors] = (18, 20)
+    peak_time[core_pixel], peak_time[core_neighbors] = (0.0, 0.0)
 
     mask = cleaning.time_clustering(
         geom,
@@ -358,6 +358,48 @@ def test_time_cleaning():
 
     assert np.count_nonzero(mask) == 0
 
+    mask = cleaning.time_clustering(
+        geom,
+        charge,
+        peak_time,
+        eps=1.0,
+        space_scale_m=0.25,
+        time_scale_ns=4.0,
+        hard_cut_pe=1,
+        minpts=5,
+    )
+
+    assert np.count_nonzero(mask) == 7
+
+    mask = cleaning.time_clustering(
+        geom,
+        charge,
+        peak_time,
+        eps=1.0,
+        space_scale_m=0.25,
+        time_scale_ns=4.0,
+        hard_cut_pe=1,
+        minpts=8,
+    )
+
+    assert np.count_nonzero(mask) == 0
+
+    charge[core_pixel], charge[core_neighbors] = 15, 5
+    peak_time[core_pixel], peak_time[core_neighbors] = (0.0, 30.0)
+
+    mask = cleaning.time_clustering(
+        geom,
+        charge,
+        peak_time,
+        eps=1.0,
+        space_scale_m=0.25,
+        time_scale_ns=4.0,
+        hard_cut_pe=1,
+        minpts=5,
+    )
+
+    assert np.count_nonzero(mask) == 6
+
 
 def test_time_constrained_clean():
     geom = CameraGeometry.from_name("LSTCam")