In this paper, a novel dissimilarity-analysis-based method is proposed to monitor the control performance of multi-input-multi-output systems. The proposed approach detects changes in the orientation and volume of hyper-ellipsoids formed by the covariance matrices via analyzing the eigenvalues of transformed covariance matrices. Furthermore, a new performance index is used to quantify performance change of control systems. Simulation results from a numerical example, the Wood Berry distillation column example, and pilot-scale experiment results all demonstrate the effectiveness of the proposed method.