This paper is concerned with model validation for industrial model predictive control systems. A new detection statistic is derived for validation of the plant model regardless of how the disturbance model changes. By appropriate filtering of process data, it is shown that performance of the on-line model validation and change detection algorithm can be improved. The proposed algorithm is illustrated by simulated examples as well as applications to model validation of an industrial model predictive control system.