Multivariate controller performance assessment (MVPA) has been developed over the last several years, but its application in advanced model predictive control (MPC) has been limited mainly due to issues associated with comparability of the variance control objective and that of MPC applications. MPC has been proven as one of the most effective advanced process control (APC) strategies to deal with multivariable constrained control problems with an ultimate objective toward economic optimization. Any attempt to evaluate MPC performance should therefore consider constraints and economic performance. In this work, we show that the variance based performance assessment may be transferred to performance assessment of MPC applications. The MPC economic performance can be evaluated by solving benefit potentials through either variability reduction of quality output variables or tuning of constraints. Algorithms for MPC performance assessment and constraint/variance tuning guidelines are developed through linear matrix inequalities (LMIs) using routine operating process data plus the process steady-state gain matrix. The proposed approach for MPC economic performance evaluation is illustrated and verified via a simulation example of an MPC application as well as a pilot-scale experiment.