Many situations arise in engineering where it is desired to model a system of complicated input and output variables. However, analytical difficulties arise when these systems exhibit nonlinear behavior. Neural networks have proven useful for such applications because they are able to model complicated nonlinear systems through exposure to a database including input parameters and the desired outputs. One such complicated system consists of the unknown relationships between flight variables and structural loads on helicopters. The development of an accurate neural network based model would allow indirect monitoring of these loads so that fatigue-damaged components could be replaced according to load history. In this thesis, an extensive database of real-time flight records has been effectively used to teach a multilayer feedforward artificial neural network nonlinear relationships between common flight variables and the resulting component loads. The trained network predicts time-varying mean and oscillatory load records corresponding to flight variable histories. Component loads in both the fixed and rotating systems of a military helicopter have been resolved over a variety of standard maneuvers. Predictions under the present conditions are on the order of 90 to 100% accurate. Although the range of maneuvers presently considered is rather limited in comparison to the total helicopter flight spectrum, the present results justify further pursuit of this neural network application. / Master of Science