Abnormal Situation Management (ASM) has received considerable attention from industry and academia recently. The first step towards better ASM is the timely detection and diagnosis of the abnormal situation. Most of the existing methods for fault diagnosis assume that only a single fault occurs at any given time. However, multiple faults do occur in processes, albeit less frequently than single faults. When multiple faults occur, existing methods either lead to incorrect diagnosis or complete lack of diagnosis. Multiple fault diagnosis (MFD) is a difficult problem because the number of combinations grows exponentially with the number of faults. In this paper, a signed directed graph (SDG) based algorithm for MFD is developed. The computational complexity is efficiently handled by assuming that the probability of occurrence of a multiple fault scenario decreases with an increasing number of faults involved. SDG based diagnosis, like any other qualitative method, has poor resolution. This poor resolution is overcome by using a knowledge base consisting of knowledge about the process constraints, maintenance schedules etc. The proposed algorithm is implemented in Gensym’s expert system shell, G2. The application of the algorithm is illustrated using an industrial scale simulation of the standard FCCU called TRAINER.