This study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a benchmark and monitor model predictive control performance on-line. A performance measure based on the ratio of historical and achieved performance is used for monitoring and a ratio of design and achieved performance is used for diagnosis. Performance monitoring and diagnosis of causes for poor performance are integrated. A real-time knowledge-based system is developed to supervise monitoring and diagnosis activities. Case studies with linear and nonlinear models of an evaporator illustrate the methodology and limitations of linearity assumptions.