Industrial processes are usually operated under normal conditions during daily production. For the purpose of improving product quality and productivity, the process engineers need to realize where the process variations come from. In this paper. according to the geometric shapes of clusters on the principal component (PC) subspace, the major variations along variable directions can be identified using a Pareto diagram and the 80/20 rule. In addition, fault isolation charts on the PC subspace are proposed to locate faulty variables when detecting abnormal events. A simulation example is demonstrated where the proposed approach is capable of locating the faulty variables without a smearing effect even in the case of multiple sensor faults. An industrial application of assessing operational performance and isolating faulty variables is presented. The results show that the faulty variables are highly correlated to the variables dominating the data variations in the normal operations, i.e., process-related abnormalities can be prevented by reducing the process variations during normal operations.