This paper presents an adaptive or self-learning filter design intended for use in real-time closed-loop pointing control systems engaging multiple targets. The design approach uses a performance index (based on the Mahalanobis generalized distance function) and multiple filters processed in parallel using the same nonlinear measurements as input. Application of performance index criteria to the statistics of individual filter residuals allows the selection of the optimum filter set without the delays typically encountered and thereby allows the composite filter structure to adapt to (or self-learn) uncertainties in modeling target acceleration capabilities. An advantage of this approach is that it also gives an operator (or robotic controller) the confidence level oftracking system performance against a maneuvering target. This information is of interest for deployment of countermeasures (e.g., fire control eventing, alarms, or engagement priority) or simply for laboratory system tests of design adequacy.