Offshore oil and gas production, platforms are uniquely hazardous in that operating personnel have to work in a perilous environment surrounded by extremely flammable hydrocarbons. A failure in an equipment could quickly propagate to others resulting in leaks, fires and explosions, causing loss of life, capital invested and production downtime. A method for preventing such accidents is to deploy intelligent monitoring tools which continuously supervise the process and the health of equipments to provide context-specific decision support to operators during safety-critical situations. A dynamic model of an offshore oil and gas production platform was developed using gPROMS and data to reflect operating conditions under normal, fault conditions and maintenance activities were simulated. These data are used to train three monitoring algorithms based on multivariate statistics (Principal Component Analysis), two of which are specialized in monitoring certain sections of the platform. These multivariate monitoring algorithms are considered as individual agents and the results produced by each are then integrated using a multi-agent collaborative framework. A consolidator agent, which uses voting based, Bayesian probability and Dempster Shafer fusion strategies for conflict resolution and decision fusion is developed. The ability of this agent based monitoring scheme to detect and diagnose faults in a more precise manner than any single FDI agent in offshore oil and gas production platforms is demonstrated. © 2009 Elsevier B.V. All rights reserved.