diff --git a/pylot/core/pick/picker.py b/pylot/core/pick/picker.py
index af17acf6..b73819a1 100644
--- a/pylot/core/pick/picker.py
+++ b/pylot/core/pick/picker.py
@@ -326,14 +326,17 @@ class AICPicker(AutoPicker):
             dataslope = aicsmooth[pickindex: iaicmax]
             # calculate slope as polynomal fit of order 1
             xslope = np.arange(0, len(dataslope), 1)
-            P = np.polyfit(xslope, dataslope, 1)
-            datafit = np.polyval(P, xslope)
-            if datafit[0] >= datafit[-1]:
-                print('AICPicker: Negative slope, bad onset skipped!')
-            else:
-                self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
-                # normalize slope to maximum of cf to make it unit independent
-                self.slope /= aicsmooth[iaicmax]
+            try:
+                P = np.polyfit(xslope, dataslope, 1)
+                datafit = np.polyval(P, xslope)
+                if datafit[0] >= datafit[-1]:
+                    print('AICPicker: Negative slope, bad onset skipped!')
+                else:
+                    self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
+                    # normalize slope to maximum of cf to make it unit independent
+                    self.slope /= aicsmooth[iaicmax]
+            except ValueError as e:
+                print("AICPicker: Problems with data fitting! {}".format(e))
 
         else:
             self.SNR = None
diff --git a/pylot/core/pick/utils.py b/pylot/core/pick/utils.py
index 9929e5bb..29b35b59 100644
--- a/pylot/core/pick/utils.py
+++ b/pylot/core/pick/utils.py
@@ -270,8 +270,13 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
             islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
             # calculate slope as polynomal fit of order 1
             xslope1 = np.arange(0, len(xraw[islope1]), 1)
-            P1 = np.polyfit(xslope1, xraw[islope1], 1)
-            datafit1 = np.polyval(P1, xslope1)
+            try:
+                P1 = np.polyfit(xslope1, xraw[islope1], 1)
+                datafit1 = np.polyval(P1, xslope1)
+            except ValueError as e:
+                print("fmpicker: Problems with data fit! {}".format(e))
+                print("Skip first motion determination!")
+                return FM
 
         # now using filterd trace
         # next zero crossings after most likely pick