In this paper, we address the problem of motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with consistent motion across image sequences. Using this assumption, we formulate the problem of motion recognition as a matching of corresponding events in image sequences. To enable the matching, we present and evaluate a set of motion descriptors that exploit the spatial and the temporal coherence of motion measurements between corresponding events in image sequences. As the motion measurements may depend on the relative motion of the camera, we also present a mechanism for local velocity adaptation of events and evaluate its influence when recognizing image sequences subjected to different camera motions. When recognizing motion patterns, we compare the performance of a nearest neighbor (NN) classifier with the performance of a support vector machine (SVM). We also compare event-based motion representations...