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This paper presents a supervising learning approach using Multilayer Feed Forward Neural Network(MFFN) and Radial Basis Fuction Neural Network(RBFN) to deal with fast and accurate static security assessment (SSA) and contingency analysis of a large electric power systems. The degree of severity of contingencies is measured by two scalar performance indices (PIs): Voltage-reactive power performance index, PIVQ and line MVA performance index, PIMVA. For each (N-1) contingency, thePerformancefatcat:4pguueojeff27mdd6ob2l6xz2e