Artificial bee colony (ABC) algorithm has been used in several optimization problems, including the optimization of synaptic weights from an Artificial Neural Network (ANN). However, this is not enough to generate a robust ANN. For that reason, some authors have proposed methodologies based on so-called metaheuristics that automatically allow designing an ANN, taking into account not only the optimization of the synaptic weights as well as the ANN's architecture, and the transfer function of each neuron. However, those methodologies do not generate a reduced design (synthesis) of the ANN. In this paper, we present an ABC based methodology, that maximizes its accuracy and minimizes the number of connections of an ANN by evolving at the same time the synaptic weights, the ANN's architecture and the transfer functions of each neuron. The methodology is tested with several pattern recognition problems.