Investigating the root causes of abnormal events is a crucial task for an industrial process. When process faults are detected, isolating the faulty variables provides additional information for investigating the root causes of the faults. Numerous data-driven approaches require the datasets of known faults, which may not exist for some industrial processes, to isolate the faulty variables. The contribution plot is a popular tool to isolate faulty variables without a priori knowledge. However, it is well known that this approach suffers from the smearing effect, which may mislead the faulty variables of the detected faults. In the presented work, a contribution plot without the smearing effect to non-faulty variables was derived. A continuous stirred tank reactor (CSTR) example and the industrial application were provided to demonstrate that the proposed approach is not only capable of locating different faulty variables when the fault was propagated by the controllers, but also capable of identifying the variables responsible for the multiple sensor faults. (C) 2013 Elsevier Ltd. All rights reserved.