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Autonomic computer systems react to changes in the system, including failures, load changes, and changed user behaviour. Autonomic control may be based on a performance model of the system and the software, which implies that the model should track changes in the system. A substantial theory of optimal tracking filters has a successful history of application to track parameters while integrating data from a variety of sources, an issue which is also relevant in performance modeling. This work applies extended Kalman filtering to track the parameters of a simple queueing network model, in response to a step change in the parameters. The response of the filter is affected by the way performance measurements are taken, and by the observability of the parameters.