In some cases, the measured electropherogram signals are of an unusual shape, showing for example peaks at unexpected migration times, spikes or abnormal fluctuation of the baseline. In such cases, a reliable RIN computation is not possible. Several separate neural networks were trained to recognize such anomalies and display a warning to the user or even suppress the display of a RIN number. Combining the results of the neural network for the RIN computation and the neural networks to detect anomalies, the RIN algorithm achieves a mean square error of 0.1 and a mean absolute error of 0.25 on an independent test set.