This paper analyzes the performance of neural networks for the identification and optimal control of active pneumatic suspensions of high-speed railway vehicles. It is shown that neural networks can be efficiently trained to identify the dynamics of nonlinear pneumatic suspensions, as well as being trained to work as (sub)optimal nonlinear controllers. The performance of the nonlinear suspension with the neuro-controller is compared with the performance of the suspension with an LQ controller designed after linearizing the suspension components around the equilibrium point.