Content-based human motion analysis has captured extensive concerns of researchers from the domains of computer animation, human-machine interaction, entertainment, etc. However, it is a non-trivial task due to the spatial and temporal variations in the motion data. In this paper, we propose a generalized model (GM)-based approach to model the variations and accurately recognize motion patterns. We partition the human character model into five parts, and extract the features of the submotions of each specific body part using clustering techniques. These features from the training trials in each class are combined to build the GM. We propose a new penalty based similarity measure for DTW to be used with the GMs for isolated motion recognition. On the other hand, from the GMs five body partition index maps are constructed and used for matching together with a flexible end point detection scheme during continuous motion recognition. In the experiments, we examine the effectiveness and efficiency of the approach in both isolated motion and continuous motion recognition. The results show that our proposed method has good performance compared with other state-of-the-art methods in recognition accuracy and processing speed.