Artificial Neural Networks are commonly used in pattern classification, function approximation, optimization, pattern matching, machine learning and associative memories. They are currently being an alternative to traditional statistical methods for mining data sots in order to classify data. Artificial Neural Networks are well-established technology for solving prediction and classification problems, using training and testing data to build a model. However, the success of the networks is highly dependent on the performance of the training process and hence the training algorithm. In this paper, we applied the Artificial Bee Colony (ABC) Optimization Algorithm on training feed-forward neural networks to classify different data sets which are widely used in the machine learning community. The performance of the ABC algorithm is investigated on benchmark classification problems from classification area and the results arc compared with the other well-known conventional and evolutionary algorithms. The results indicate that ABC algorithm can efficiently be used on training feed-forward neural networks for the purpose of pattern classification.