Performance measures for statistical segmentation have been developed for a space-and-time critical Bayesian statistical tracker. They are intended to become an integral part of a knowledge-based tracking algorithm, which has been developed by RCA. The performance measures are serving to quantify the usefulness of the processed input, to assist in the identification of each tracking state and give its reliability, and to predict impending changes of state. They have been tested using stochastically generated target-background frames. Performance measure results have correlated well with the parameters which characterize the difference in the target and background distributions. A host of possible performance measures are discussed in relation to their strengths and weaknesses. Experimental results for the measures currently being employed by RCA are given, and areas for future research are indicated.