The purpose of this paper is to present a simplified method to estimate aircraft fuel consumption using an artificial neural network. The models developed here are can be implemented in fast-time airspace and airfield simulation models. A representative neural network aided fuel consumption model was developed using data given in the aircraft performance manual. The data used in this study was applicable to the Fokker 100 aircraft powered by Rolls-Royce Tay 650 engines. A second data set was applied to the SAAB 2000 turboprop aircraft with good results. The methodology can be extended to any type of aircraft including piston and turboprop type vehicles with confidence. The neural network was trained to estimate fuel consumption of an example aircraft. Results were compared to the actual performance provided in the aircraft performance manual and found to be accurate for possible implementation in fast-time simulation models. The result from the neural network model was compared with analytical models. The results of this study illustrate that a threelayer artificial neural network with nonlinear transfer functions can accurately represent complex aircraft fuel consumption functions for climb, cruise and descent phases of flight.