This paper describes a system which uses multiple visual processes to detect and track hands for gesture recognition and human-computer interaction. This system is based on an architecture in which a supervisor selects and activates visual processes. Each process provides a confidence factor which makes it possible for the system to dynamically reconfigure itself in response to events in the scene. Visual processes for hand tracking are described using image differencing and normalized histogram matching. The result of hand detection is used by a recursive estimator (Kalman filter) to provide an estimate of the position and size of the hand. The resulting system provides robust and precise tracking which operates continuously at approximately 5 images per second on a 150 megahertz Silicon Graphics Indy.