Storm Movement Prediction (SMP) is a proposed (future) product for Terminal Doppler Weather Radar (TDWR), aiding controllers by tracking storms approaching and passing through the terminal environment. Because the scan strategy (data acquisition) of TDWR has been critically designed to meet the needs of its primary function, which is the detection of hazardous low-altitude wind shear, there is the question of whether reliable storm tracking can be obtained from the TDWR data set. The objectives of storm tracking involve a scope (spatial range) much larger than that required for the wind-shear algorithms where volume coverage is confined (in off-airport sited radars) to a sector covering the important approach and departure corridors and the only 360-degree scans are near-surface scans for gust-front detection. This report examines the application of a correlation based method of detecting storm motion, testing the notion that reliable storm motion can be inferred from existing TDWR data. In particular, storm motion derived from an analysis of the TDWR Precipitation product (PCP) is studied. A summary description of the algorithm is presented along with an analysis of its performance using data from MIT Lincoln Laboratory's TDWR testbed operations in Denver (1988) and Kansas City (1989). The primary focus of the present analysis is on the reliability of tracking, since the algorithm is expected to operate in an autonomous environment. Some attention is given to the idea of prediction, in the form of storm extrapolation, considering 15, 30, and 60 minute predictions. Specific areas for improvement are identified, and application of hte algorithm track vectors for long-term prediction (30-60 minutes) is discussed with reference to example PCP images.