In this paper, it is proposed to develop the fault diagnosis system using the principal component analysis (PCA) for the intelligent operation support system that calculates the effect of fault propagation in abnormalities situation and gives appropriate information to plant operators. This proposed system using PCA discriminates a failure of equipment based on process variables. The proposed method deals with process variables in steady condition and only one type warning alarm condition that is occurred by several different failures. A set of process variables on each failure is shown as the points on 2dimensional data space by PCA. This system judges as a failure of the equipment when a set of current process variables is closed to the point of a failure of equipment on the data space. The proposed fault diagnosis system is applied to process on a simulator and is confirm its validity. Chemical plants had become very complex for a lot of instrumentation and control systems. It is difficult for operators to predict the effect of fault and to decide corrective actions. In addition, quick response from operator is demanded as any delay response for abnormalities may expand the damage of chemical plant.� Many sensors are required to monitor the chemical process for safety. It is difficult to detect the fault in the process from too many sensors. Therefore many sensors’ data are reduced to less parameter by a multivariate analysis (Kano et al. 2001, 2004, Wise and Gallagher 1996). These methods detect the fault in the process, but not diagnosis the failure of equipment. Our research is developing “Intelligent Operation Support System”. This system calculates the effect of fault propagation in abnormal situations and gives appropriate information to operators. It will help operators to make quick judgments for safety. This system predicts process variables in the abnormalities situation’s plant using a simulator. The states of all equipment are inputted to a simulator in order to calculate process variables correctly. In this paper, it is proposed to develop the fault diagnosis system using the principal component analysis. This proposed system discriminates a failure of equipment in its early stage. This system is important for the intelligent operation support system. The principal component analysis discriminates a failure of equipment based on process variables from the actual plant. A data set is process variables in steady conditions and in abnormalities condition. These data sets are obtained from a simulator when the warning alarm is turned on after the selected equipment is artificially changed to a failure mode. The proposed method deals with process variables in steady condition and only one type warning alarm condition at a time. One type warning alarm is occurred by several different failures, so process variables in one failure are similar to another failure in one type warning alarm. So it is difficult to distinguish one failure among failures in one type warning alarm when the principal component analysis deals with failures in all warning alarm at a time. And the proposed method deal with selected process variables based on value changes after a failure is occurred. A set of process variables on each failure is shown as the points on 2dimensional data space by the principal component analysis. This system judges as a failure of the