Neural network applications to aircraft automatic landing control based on linearized inverse aircraft model are presented. Conventional automatic landing systems can provide a smooth landing which is essential to the comfort of passengers. However, these systems work only within a specified operational safety envelope. When the conditions are beyond the envelope, such as turbulence or wind shear, they often cannot be used. The objective of this study is to investigate the use of neural networks with linearized inverse aircraft model in automatic landing systems and to make these systems more intelligent. Current flight control law is adopted in the intelligent controller design. Tracking performance and robustness are demonstrated through software simulations. This paper presents five different neural network controllers to improve the performance of conventional automatic landing systems based on the linearized inverse aircraft model. Simulation results show that the neural network controller can successfully expand the safety envelope to include more hostile environments such as severe turbulence.