In this paper the capabilities of an incoherent radar sensor network for robust Doppler-based gesture recognition are investigated and a significant performance boost is demonstrated. A comprehensive dataset is recorded with an incoherent sensor network consisting of three time-synchronized 77 GHz FMCW radars. Based on this dataset, we show that differential Doppler features obtained from the varying viewing angles result in a significant multistatic gain for classification, particularly for high intra-class variations and low Doppler frequencies. For the most complex dataset crossuser validation accuracy of a CNN with optimized data fusion is improved by 7.4 % to an overall value of 87.1 %, which we regard high as gestures are not designed for distinguishability but reflect everyday control and communication signals.