A design of a multivariate knowledge-based fault diagnosis system is described in this paper. The proposed design is based on a novel strategy, which integrates multivariate statistical process control (MSPC) monitoring into knowledge-based (KB) fault diagnosis both qualitatively and quantitatively using expert system technology. The integration mechanism mimics how process engineers combine their process knowledge with Principal Component (PC) score contribution, PC score deviation contribution and square predicted error (SPE) contribution of principal component analysis (PCA) projection in diagnosing anomaly. The system has been successfully implemented in G2 environment. A dynamic simulation of a continuous stirred tank reactor (CSTR) running a second order exothermic reaction was used to test the proposed system. Testing results clearly indicated that the system produces more contrasting probabilities between all possible exogenous causes and it can give accurate diagnosis when the process upsets were undetected by univariate monitoring.