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A neural network-based engine performance, fuel efficiency and emissions prediction system has been developed for both spark-ignited and compression ignition engines. Through limited training on an engine dynamometer, the neural network system is able to predict accurately real-time engine power output, fuel consumption and regulated exhaust emissions using readily measured engine parameters, across highly transient engine operating cycles. Applications for the models developed using this process include engine diagnostics, virtual sensing of unmeasured or unmeasurable engine emissions, engine control, and engine and vehicle modeling. Results from the prediction of the performance and emissions from a 300 hp CIDI engine and a 120 hp SI engine are presented, showing the potential of this newly developed approach.