In this paper, we compared fuzzy and Kalman-filter control systems for real-time target tracking. Both systems performed well in the presence of additive measurement noise. In the presence of mild process (unmodelled-effects) noise, the fuzzy system exhibited finer control. We tested the robustness of the fuzzy controller by removing random subsets of fuzzy associations or ‘rules’, and by adding destructive or ‘sabotage’ fuzzy rules to the fuzzy system. We tested the robustness of the Kalman tracking system by increasing the variance of the unmodelled-effects noise process. The fuzzy controller performed well until we removed over 50% of the fuzzy rules. The Kalman controller's performance quickly depreciated as the unmodelled-effects variance increased. We used unsupervised neural-network learning to adaptively generate the fuzzy controller's fuzzy-associative-memory structure. The fuzzy systems did not require a mathematical model of how system outputs depended on inputs.