In this paper we examine feed-forward neural networks using genetic algorithms in the training process instead of error backpropagation algorithm. Additionally real encoding is preferred to binary encoding as it is more appropriate to find the optimum weights. We use learning and momentum rates for the weight updating as in the case of the error backpropagation algorithm. Some empirical examples as well as the programming routine in MATLAB are provided.