Piloted simulation and flight-test results from the evaluation of a reconfigurable flight control law for the X-36 tailless fighter aircraft are presented. The reconfigurable control law, which is based on dynamic inversion in an explicit model following framework, employs an online neural network to adaptively regulate the error in the plant inversion, which may be due to modeling uncertainties, failures, or damage. Simulated actuator failures, which caused effectors to be locked at prescribed positions, were injected to evaluate the stability and handling qualities of the reconfigurable control law under failures. The control law was not given any knowledge of the actuator failure, and fault detection logic was not employed. Piloted simulation testing was used to compare the reconfigurable control law to the X-36 dynamic inversion control laws under simulated actuator failures. The reconfigurable control law showed improved handling qualities and departure resistance relative to the baseline X-36 control laws.