This thesis details efforts made to perform system identification and control of aircraft using a self-assembling radial basis neural network method. First, Sequential Function Approximation (SFAX) is presented as an efficient, accurate and reliable tool for system identification and used to create and train a neural network model of an aircraft’s longitudinal and lateral-directional dynamics, using only input and output data. This is demonstrated on data from a small unconventional UAV design called ICE as well as from the T-38 jet flight test aircraft. Then, this neural network model is applied to a model predictive control scheme using MATLAB optimization routines. Finally, SFAX is used to build a direct inverse neural network controller.