The control and analysis of magnetic bearings has been primarily based upon classical linear control theory. This approach does not allow for some important system complexities and nonlinearities to be taken into account. The resulting simplifications degrade the overall system performance. This paper investigates the use of a neural network to control a magnetic bearing flywheel energy storage system. A plant simulation is developed as well as a neural network emulator and controller.