Model-based controllers contain two elements that must be adjusted to maintain desired performance : parameters of the process model and a tuning parameter in the controller design equation. A unified framework is presented where vector quantizing networks are used in pattern-based methods for diagnosing process excitation and controller performance. Excitation diagnostics identify sufficiently excited dynamic process data for model updating. Performance diagnostics analyze setpoint response data and determine appropriate updates to the tuning parameter. Supervisory adaptation logic enables these two adaptive mechanisms to work together to maintain model accuracy and desired controller performance. The method is general to a number of model-based control algorithms and process model forms. Demonstrations employ a FOPDT model, as well as both the Pl and DMC algorithms for set point tracking and disturbance rejection in a simulation and a bench-scale process.