Fault propagation analysis is the cornerstone to assure safe operation, optimized maintenance, as well as for the management of abnormal situations in chemical and petrochemical plants. Due to plant complexity and dynamic changes in plant conditions, current approaches have major limitations in identifying all possible fault propagation scenarios. This is due to the lack of realistic equipment and fault models. In this paper, practical framework is proposed to synthesize and assess all possible fault propagation scenarios based on robust modeling methodology. Fault models are constructed where deviations are identified and associated with symptoms, faults, causes, and consequences. Fault models are tuned using real time process data, simulation data, and human experience. The proposed system is developed and applied on case study experimental plant. (c) 2007 Elsevier Ltd. All rights reserved.