A design of attitude controller for a tiltrotor is presented augmenting L1 adaptive control, neural networks, and feedback linearization. The neural networks compensate for the modeling error caused by the lack of knowledge of tiltrotor dynamics while the L1 adaptive control allows high adaptation gains in adaptation laws thereby, satisfying tracking performance requirement. The efficacy of this control methodology is illustrated in high-fidelity nonlinear simulation of a tiltrotor by flying the tiltrotor in different flight modes from where the L1 adaptive controller with neural networks is originally designed for.