diff --git a/src/aspire/utils/matrix.py b/src/aspire/utils/matrix.py
index d5d495365c..f48f9bbac5 100644
--- a/src/aspire/utils/matrix.py
+++ b/src/aspire/utils/matrix.py
@@ -336,6 +336,15 @@ def nearest_rotations(A, allow_reflection=False):
         #     R = (U * -1 @ VT) * [-1,-1,1]
         #     R =  (U @ VT) * (-1 * [-1,-1,1])
         #     R =  U @ (VT * [1,1,-1])
+        #
+        # See:
+        #
+        #       Gower, J.C. (1976), Procrustes rotation problems. The
+        #       Mathematical Scientist, 1 (Supplement), 12-15.
+        #
+        #       Ten Berge, J.M.F. (2006), The Rigid Orthogonal Procrustes
+        #       Rotation Problem. Psychometrika, vol. 71, no., 1, 201-205.
+
         d = np.array([1, 1, -1], dtype=dtype)
         neg_det_idx = np.linalg.det(U) * np.linalg.det(VT) < 0
         VT[neg_det_idx] = VT[neg_det_idx] * d
diff --git a/src/aspire/utils/rotation.py b/src/aspire/utils/rotation.py
index 2a2e0f98a0..720b9a7898 100644
--- a/src/aspire/utils/rotation.py
+++ b/src/aspire/utils/rotation.py
@@ -10,6 +10,7 @@
 from scipy.spatial.transform import Rotation as sp_rot
 
 from aspire.utils.random import Random
+from aspire.utils.matrix import nearest_rotations
 
 
 class Rotation:
@@ -107,8 +108,8 @@ def find_registration(self, rots_ref):
 
         :param rots_ref: The reference Rotation object to which we would like to align
             with data matrices in the form of a n-by-3-by-3 array.
-        :return: o_mat, optimal orthogonal 3x3 matrix to align the two sets;
-                flag, flag==1 then J conjugacy is required and 0 is not.
+        :return: Q, optimal orthogonal 3x3 matrix to align the two sets;
+                J_conj, specifies whether J-conjugacy is needed.
         """
         rots = self._matrices
         rots_ref = rots_ref.matrices.astype(self.dtype)
@@ -129,44 +130,34 @@ def find_registration(self, rots_ref):
             Q1 = Q1 + R @ Rref.T
             Q2 = Q2 + (J @ R @ J) @ Rref.T
 
-        # Compute the two possible orthogonal matrices which register the
-        # estimated rotations to the true ones.
-        Q1 = Q1 / K
-        Q2 = Q2 / K
+        Q1 = nearest_rotations(Q1)
+        Q2 = nearest_rotations(Q2)
 
         # We are registering one set of rotations (the estimated ones) to
         # another set of rotations (the true ones). Thus, the transformation
         # matrix between the two sets of rotations should be orthogonal. This
         # matrix is either Q1 if we recover the non-reflected solution, or Q2,
-        # if we got the reflected one. In any case, one of them should be
-        # orthogonal.
+        # if we got the reflected one.
 
-        err1 = norm(Q1 @ Q1.T - np.eye(3), ord="fro")
-        err2 = norm(Q2 @ Q2.T - np.eye(3), ord="fro")
+        err1 = np.sum([norm(Q1.T @ R - Rref, "fro") ** 2
+                       for R, Rref in zip(rots, rots_ref)])
+        err2 = np.sum([norm(Q2.T @ (J @ R @ J) - Rref, "fro") ** 2
+                       for R, Rref in zip(rots, rots_ref)])
 
-        # In any case, enforce the registering matrix O to be a rotation.
         if err1 < err2:
-            # Use Q1 as the registering matrix
-            U, _, V = svd(Q1)
-            flag = 0
+            return Q1, False
         else:
-            # Use Q2 as the registering matrix
-            U, _, V = svd(Q2)
-            flag = 1
+            return Q2, True
 
-        Q_mat = U @ V
-
-        return Q_mat, flag
-
-    def apply_registration(self, Q_mat, flag):
+    def apply_registration(self, Q, J_conj):
         """
         Get aligned Rotation object to reference ones.
 
         Calculated aligned rotation matrices from the orthogonal transformation
         that best aligns the estimated rotations to the reference rotations.
 
-        :param Q_mat:  optimal orthogonal 3x3 transformation matrix
-        :param flag:  flag==1 then J conjugacy is required and 0 is not
+        :param Q:  optimal orthogonal 3x3 transformation matrix
+        :param J_conj:  whether J-conjugacy is required
         :return: regrot, aligned Rotation object
         """
         rots = self._matrices
@@ -178,9 +169,9 @@ def apply_registration(self, Q_mat, flag):
         regrot = np.zeros_like(rots)
         for k in range(K):
             R = rots[k, :, :]
-            if flag == 1:
+            if J_conj:
                 R = J @ R @ J
-            regrot[k, :, :] = Q_mat.T @ R
+            regrot[k, :, :] = Q.T @ R
         aligned_rots = Rotation(regrot)
         return aligned_rots
 
@@ -192,8 +183,8 @@ def register(self, rots_ref):
             to align with data matrices in the form of a n-by-3-by-3 array.
         :return: an aligned Rotation object
         """
-        Q_mat, flag = self.find_registration(rots_ref)
-        return self.apply_registration(Q_mat, flag)
+        Q, J_conj = self.find_registration(rots_ref)
+        return self.apply_registration(Q, J_conj)
 
     def mse(self, rots_ref):
         """