The aim of this work is to present a proposal for implementation of a support framework for abnormal situation management in the chemical process industry. A main feature of the technology developed is that it takes advantage of existing software packages that are familiar to plant engineers (e.g. Plant Information System) and a commercial process simulator. On the basis of three sources of information (a historical database, a HAZOP analysis, and a first principles plant model), the support framework is developed and easily implemented into the real plant. It consists of a preprocessing module, which performs a variety of key tasks using plant data such as data reconciliation, filtering, and denoising. Some of the outputs of this preprocessing module are the inputs of the fault diagnosis system (FDS). This FDS is a combination of a pattern recognition approach based on neural networks and a fuzzy logic system FLS) in a block oriented configuration. The case study used to demonstrate the FDS implementation corresponds to a real petrochemical plant.