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This paper presents a new approach for finding the sequence of events that may lead to catastrophic failure in a power system. The probable sequences (of events) leading to catastrophic failures are identified using risk indices which incorporate the severity as well as the probability of the contingencies. Probable collapse sequences are identified offline for different possible loading conditions using a modified fast decoupled load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics and stored in a knowledge base. Pattern recognition method and fuzzy estimation are used for online identification of collapse sequences for any operating condition from the stored database (knowledge base).