Advancing the USAF's capabilities in engine life measurement and diagnostic monitoring of critical engine components is necessary to improve engine availability, minimize performance degradation, and reduce life cycle costs. Proven artificial intelligence (AI) technologies such as neural networks, fuzzy logic and expert systems present an opportunity to significantly enhance current trending and diagnostic capabilities in a real-time monitoring environment.This paper outlines the strategy adopted by the USAF to develop a state-of-the-art engine health monitoring system. In addition, the status of an R&D program whose ultimate aim is to demonstrate the perceived capability is also discussed. Engine data currently sensed and recorded for post flight processing will be analyzed in a continuous real-time mode. For fault detection and accommodation, extensive knowledge of how a healthy engine operates under given conditions will be analyzed, and any deviation from this “normal” pattern of expected parameters will be detected and further analyzed. Faults resulting from sensor failure modes will be promptly isolated and more complex faults will be identified by reasoning utilizing fuzzy logic and pattern recognition schemes. The same sensed data will be used as inputs to the life measurement module of the monitoring system where life usage algorithms will determine critical component remaining life based on actual mission severity. The system under development will be based on the Rolls-Royce T45 engine (Adour) which is fitted to the Navy's F-405 trainer, and a full-scale demonstration of the technology will ultimately be conducted on this engine.