Motion-based recognition deals with the recognition of an object or its motion based on motion in a sequence of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consists of a complex and coordinated series of events that cannot be understood by looking at only a few frames. This paper provides a review of recent developments in the computer vision aspect of motionbased recognition. We will identify two main steps in motionbased recognition. The first step is the extraction of motion information and its organization into motion models. The second step consists of the matching of some unknown input with a constructed model. Several methods for the recognition of objects and motions will then be reported. They include methods such as cyclic motion detection and recognition, lipreading, hand gestures interpretation, motion verb recognition and temporal textures classification. Tracking and recognition of human motion, like walking, skipping and running will also be discussed. Finally, we will conclude the paper with some thoughts about future directions for motionbased recognition.