We present the results of using Hidden Markov Models (HMMs) for automatic segmentation and recognition of user motions. Previous work on recognition of user intent with man/machine interfaces has used task-level HMMs with a single hidden state for each sub-task. In contrast, many speech recognition systems employ HMMs at the phoneme level, and use a network of HMMs to model words. We analogously make use of multi-state, continuous HMMs to model action at the "gesteme" level, and a network of HMMs to describe a task or activity. As a result, we are able to create a "task language" that is used to model and segment two different tasks performed with a human-machine cooperative manipulation system. Tests were performed using force and position data recorded from an instrument held simultaneously by a robot and human operator. Experimental results show a recognition accuracy exceeding 85%. The resulting information could be used for intelligent command of virtual and teleoperated environments, and implementation of contextually appropriate virtual fixtures for dynamic operator assistance while executing complex tasks.