Behavior recognition can be greatly levitated by a model of the task with which the behavior is associated. We present a Markovian task model that captures the temporal relations between subtasks, and provides prior information that helps behavior recognition from simple and noisy sensory data. A Dynamic Bayesian Network is used to integrate multi-model evidence and infer underlying task states. Experiments demonstrate that this system can recognize human behavior in everyday tasks such as making sandwiches.