This paper studies the application of robust state-space estimation with uncertain models to tracking problems in human-machine interfaces. The need for robust methods arises from the desire to control the influence of uncertain environmental conditions on system performance, such as the effect of abrupt variations in object speed and motion characteristics. This paper produces models for motion uncertainties associated with a human hand, and applies them to a robust state-space estimation algorithm used to track a user's pointing fingertip. Then a comparison is performed between the results from the robust tracker against a Kalman filter.