To maintain desired controller performance in the presence of process nonlinearity and non-stationarity, linear model-based control strategies become dependent upon the regular updating of a process model. This paper explores the use of a passive adaptive algorithm which updates the process model in a closed loop by taking advantage of naturally occurring dynamic events rather than by injecting perturbations into the system to create dynamic events. Such closed-loop identification is possible, but it requires that these events contain process information that is not masked by measurement noise or unmeasured disturbances. Presented here is a pattern-based excitation diagnostic tool (EDT) that determines when sufficient process excitation exists for model updating. The EDT consists of vector quantizing neural networks (VQNs) similar to the ART2-A and a decision maker that is a simple set of rules. The VQNs are trained to recognize local dynamic behavior in the recent histories of each process variable. The decision maker uses the outputs from these VQNs to diagnose when sufficient dynamics exist for model updating. Details of the EDT are presented along with several challenging demonstrations on both simulated and real single-input single-output processes.