A model for learning human movement is proposed. The learning model generates plausible trajectories of limbs that mimic the human movement. The learning model is able to generalize these trajectories over extrinsic constraints. These constraints result from the space of start and end configuration of the human body and task-specific constraints such as obstacle avoidance. This generalization is a step forward from existing systems that can learn single gestures only. Such a model is needed to develop humanoid robots that move in a human-like way in reaction to diverse changes in their environment. The model proposed to accomplish this uses a combination of principal component analysis (PCA) and a special type of a topological map called the dynamic cell structure (DCS) network. Experiments on a kinematic chain of 3 joints show that this model is able to successfully generalize movement using a few training samples for both free movement and obstacle avoidance.