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Recently, the eniergeiice of neural networks as a promising tool for approximating complex system input-output mappings has generated a great (leal of interest in the area of inodeling, identification and control of noiiliiiear dynamical systeins. One specific research are8 that woiild tieiiteticlously beiiefit from this approach is the area of identification and control of Iiiglt performance aircraft, especially at high angles of attack. At those flight conditions, the control task becomes est.remclq dificult due to added design complexity and hard nonlinearities characterizing the system. In this paper, we investigate one type of neiiml iietworks, iianiely the Radial Basis Fniictioii (RBF) networks, and apply lliein to the identification and control problems of ari aircraft system. The RBI: network is used as an on-line appiosiniator of the aircraft pitch dynamics, conibinecl with a nonlinear cotit rol law to iiiipiove the closecl-loop system perforinanre. Tlie results are illustrated through simulations using a nonlinear model of the F-18 aircraft pitch dynamics.