This chapter presents an evolutionary method for creating an artificial neural network based controller for an autonomous land vehicle. Previous studies which have used evolutionary procedures to evolve artificial neural networks have been constrained to small problems by extremely high computational costs. In this chapter, methods for reducing the computational burden are explored. Previous connectionist based approaches to this task are discussed. The evolutionary algorithrm used in this study, Population-Based Incremental Learning (PBIL), is a variant of the traditional genetic algorithm. It is described in detail in this chapter. The results indicate that the evolutionary algorithm is able to generalize to unseen situations better than the standard method of error backpropagation; an improvement of approximately 18% is achieved on this task. The networks evolved are efficient; they use only approximately half of the possible connections. However, the evolutionary algorithm may require considerably more computational resources on large problems.