Neural network augmented model inversion control is used to provide a civilian tilt-rotor aircraft with consistent response characteristics throughout its operating envelope, including conversion flight. The implemented response types are Attitude Command Attitude Hold in the longitudinal channel, and Rate Command Attitude Hold about the roll and yaw axes. This article describes the augmentation in the roll channel and the augmentation for the yaw motion including Heading Hold at low airspeeds and automatic Turn Coordination at cruise flight. Conventional methods require extensive gain scheduling with tilt-rotor nacelle angle and airspeed. A control architecture is developed that can alleviate this requirement and thus has the potential to reduce development time. It also facilitates the implementation of desired handling qualities, and permits compensation for partial failures. One of the most powerful aspects of the controller architecture is the accommodation of uncertainty in control as well as in the states. It includes an online, i.e. learning-while-controlling, neural network. Lyapunov analysis guarantees the boundedness of tracking errors and network parameters. The performance of the controller is demonstrated using the nonlinear Generic Tilt-Rotor Simulation code developed for the Vertical Motion Simulator at the NASA Ames Research Center.