diff --git a/stingray/crosscorrelation.py b/stingray/crosscorrelation.py
index bb42cc03d..8d5b196da 100644
--- a/stingray/crosscorrelation.py
+++ b/stingray/crosscorrelation.py
@@ -76,7 +76,6 @@ class CrossCorrelation(object):
     References
     ----------
     .. [scipy-docs] https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.signal.correlate.html
-
     """
 
     def __init__(self, lc1=None, lc2=None, cross=None, mode='same', norm="none"):
@@ -211,6 +210,12 @@ def cal_timeshift(self, dt=1.0):
         """
         Calculate the cross correlation against all possible time lags, both positive and negative.
 
+        The method signal.correlation_lags() uses SciPy versions >= 1.6.1 ([scipy-docs-lag]_)
+        
+        References
+        ----------
+        .. [scipy-docs-lag] https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.correlation_lags.html
+        
         Parameters
         ----------
         dt: float, optional, default ``1.0``
@@ -244,9 +249,20 @@ def cal_timeshift(self, dt=1.0):
                     self._make_corr(self.lc1, self.lc2)
 
         self.n = len(self.corr)
-        dur = int(self.n / 2)
-        # Correlation against all possible lags, positive as well as negative lags are stored
-        x_lags = np.linspace(-dur, dur, self.n)
+        
+        if self.cross is not None:
+          # Obtains correlation lags if a cross spectrum object is given
+          # Correlation against all possible lags, positive as well as negative lags are stored
+          # signal.correlation_lags() method uses SciPy versions >= 1.6.1
+          x_lags = signal.correlation_lags(self.n, self.n, self.mode)
+          
+        else:
+          # Obtains correlation lags if two light curves are porvided
+          # Correlation against all possible lags, positive as well as negative lags are stored
+          # signal.correlation_lags() method uses SciPy versions >= 1.6.1
+          x_lags = \
+              signal.correlation_lags(np.size(self.lc1.counts), np.size(self.lc2.counts), self.mode)
+        
         self.time_lags = x_lags * self.dt
         # time_shift is the time lag for max. correlation
         self.time_shift = self.time_lags[np.argmax(self.corr)]
diff --git a/stingray/tests/test_crosscorrelation.py b/stingray/tests/test_crosscorrelation.py
index 4625c2bcf..f9a1c4ef4 100644
--- a/stingray/tests/test_crosscorrelation.py
+++ b/stingray/tests/test_crosscorrelation.py
@@ -47,6 +47,10 @@ def setup_class(cls):
         cls.lc_s = Lightcurve([1, 2, 3], [5, 3, 2])
         # lc with different time resolution
         cls.lc_u = Lightcurve([1, 3, 5, 7, 9], [4, 8, 1, 9, 11])
+        # Light curve with odd number of data points
+        cls.lc_odd = Lightcurve([1, 2, 3, 4, 5], [2, 3, 2, 4, 1])
+        # Light curve with even number of data points
+        cls.lc_even = Lightcurve([1, 2, 3, 4, 5, 6], [2, 3, 2, 4, 1, 3])
 
     def test_empty_cross_correlation(self):
         cr = CrossCorrelation()
@@ -117,15 +121,15 @@ def test_crossparam_input(self):
 
     def test_cross_correlation_with_unequal_lc(self):
         result = np.array([-0.66666667, -0.33333333, -1., 0.66666667, 3.13333333])
-        lags_result = np.array([-2, -1, 0, 1, 2])
+        lags_result = np.array([-1.,  0.,  1.,  2.,  3.])
         cr = CrossCorrelation(self.lc1, self.lc_s)
         assert np.allclose(cr.lc1, self.lc1)
         assert np.allclose(cr.lc2, self.lc_s)
         assert np.allclose(cr.corr, result)
         assert np.isclose(cr.dt, self.lc1.dt)
         assert cr.n == 5
+        assert np.isclose(cr.time_shift, 3.0)
         assert np.allclose(cr.time_lags, lags_result)
-        assert np.isclose(cr.time_shift, 2.0)
         assert cr.mode == 'same'
         assert cr.auto is False
 
@@ -252,3 +256,29 @@ def test_auto_correlation_with_full_mode(self):
         assert np.isclose(ac.time_shift, 0.0)
         assert ac.mode == 'full'
         assert ac.auto is True
+        
+    def test_cross_correlation_with_identical_lc_oddlength(self):
+        result =  np.array([ 1.68, -3.36, 5.2, -3.36, 1.68])
+        lags_result = np.array([-2, -1, 0, 1, 2])
+        cr = CrossCorrelation(self.lc_odd,self.lc_odd)
+        assert np.allclose(cr.lc1, cr.lc2)
+        assert np.allclose(cr.corr, result)
+        assert np.isclose(cr.dt, self.lc_odd.dt)
+        assert cr.n == 5
+        assert np.allclose(cr.time_lags, lags_result)
+        assert np.isclose(cr.time_shift,0.0)
+        assert cr.mode == 'same'
+        assert cr.auto is False
+   
+    def test_cross_correlation_with_identical_lc_evenlength(self):
+        result =  np.array([-1.75, 2.5, -4.25, 5.5, -4.25, 2.5])
+        lags_result = np.array([-3, -2, -1, 0, 1, 2])
+        cr = CrossCorrelation(self.lc_even,self.lc_even)
+        assert np.allclose(cr.lc1, cr.lc2)
+        assert np.allclose(cr.corr, result)
+        assert np.isclose(cr.dt, self.lc_even.dt)
+        assert cr.n == 6
+        assert np.allclose(cr.time_lags, lags_result)
+        assert np.isclose(cr.time_shift,0.0)
+        assert cr.mode == 'same'
+        assert cr.auto is False