Inverse problems can be applied to aircraft in many areas. One of the disciplines within the aerospace industry with the most openly published data is in the area of aircraft performance. Many aircraft manufacturers publish performance claims, flight manuals and Standard Aircraft Characteristics (SAC) charts without any mention of the more fundamental technical information of the drag and engine data. With accurate tools, generalized aircraft models and a few curve-fitting techniques, it is possible to evaluate vehicle performance and estimate the drag, thrust and fuel consumption (TSFC) with some accuracy. This thesis is intended to research the use of aircraft performance information to deduce these aircraft--specific drag and engine models. The proposed method incorporates models for each performance metric, modeling options for drag, thrust and TSFC, and an inverse method to match the predicted performance to the actual performance. Each of the aircraft models is parametric in nature, allowing for individual parameters to be varied to determine the optimal result. The method discussed in this work shows both the benefits and pitfalls of using performance data to deduce engine and drag characteristics. The results of this method, applied to the McDonnell Douglas DC-10 and Northrop F-5, highlight many of these benefits and pitfalls, and show varied levels of success. A groundwork has been laid to show that this concept is viable, and extension of this work to additional aircraft is possible with recommendations on how to improve this technique.