Become popular among researchers and practitioners for modelling complex real--world problems. One of the latest research areas in this field is evolving ANNs. In this paper, we investigate the simultaneous evolution of architectures and connection weights in ANNs. In simultaneous evolution, we use the concept of multiobjective optimization and subsequently evolutionary multiobjective algorithms to evolve ANNs. The results are promising when compared with the traditional Backpropagation algorithm.