Although there is a large body of works on conventional target tracking techniques that are based primarily on Kalman filtering and probabilistic data association, there are very few practical techniques that can be shown to perform well under a high cluttered tracking environment. This is due to the difficulty of the combined target detection and measurement to track association problem. Furthermore, conventional techniques usually make some simplifying assumptions that are difficult to realized in practice, e.g. the clutter density is uniform, measurement noise is stationary, the target track is well defined, etc. Another weakness of the conventional techniques is that even if we have some special knowledge about target attributes, it is not easy to incorporate this knowledge into the tracking problem. This paper first presents an analysis of the target tracking problem using fuzzy logic theory. Subsequently, a number of fuzzy propositions that a fuzzy tracker can use to implement a data association algorithm are formulated. Finally, a fuzzy tracker is implemented based on the fuzzy association rules and Kalman filtering and its performance is compared against the performance of a standard PDA filter.