Abstract Control performance assessment (CPA) techniques provide an indication of how current controller performance compares with what would be considered to be ideal. The ideal performance is typically referred to as a ‘benchmark’. This paper argues that there are two fundamental requirements for any CPA algorithm. The first is that it should be able to detect any change in the performance of a control system and the second is that it should be able to identify the potential improvement that can be made to the performance of the control system if it were to be re-tuned or re-designed. The ability of current multivariable CPA techniques to address these two issues is reviewed and a novel monitoring strategy for application to multivariable control systems is proposed. The ability of this strategy to provide an improved approach to detecting changes in multivariable control performance and identifying the potential improvements that are possible through re-tuning the controller are illustrated using simulated and industrial data.