Among several machine algorithms,Feed Forward Neural Networks are one of the widely used machine learning techniques for pattern classification. Generally,to improve the obtained classification accuracy results,we optimize the parameters (weights and bias) of the neural networks. The optimization aims to minimize the mean square error (MSE) calculated using the actual output produced by the feed forward network and the desired output. The back-propagation (BP) training algorithm is the most prominent approach for optimization in supervised learning strategy. Recently,several nature inspired metaheuristic techniques are widely used for training neural networks. These techniques can be broadly categorized into Swarm-based,Bio inspired based,Physics-Chemistry based and other categories. In this paper,we review the steady improvements made over training neural networks using nature inspired metaheuristic techniques for various domains including medical,manufacturing,business,scientific,etc. © 2016,International Journal of Pharmacy and Technology. All rights reserved.