In this paper the approach for classification of acoustic emission signals to their respective sources is employed using a swarm intelligence technique called artificial bee colony. In this work, artificial bee colony is employed to train a multilayer perceptron neural network which is used for the classification of the acoustic emission signal to their respective source. Acoustic emission is carried out using pulse, pencil and spark signal source on the surface of solid steel block. The signal parameters are measured using AET 5000 system. To begin with, the complexity for acoustic emission data set is verified using conventional statistical technique like principal component analysis and traditional training algorithm like multilayer perceptron neural network trained using the backpropagation algorithm. The experiment shows in both the case the classification is not accurate. For this complex acoustic emission data set multilayer perceptron neural network trained using the artificial bee colony algorithm is applied resulting improved classification. The multilayer perceptron neural network trained using the artificial bee colony based technique has an advantage over conventional statistical techniques and traditional training algorithm because they are distribution free, i.e., no knowledge is required about the distribution of data and also they do not get stuck in local minima. To overcome error in learning and also the risk involved in misclassification, we include risk-sensitive loss function. The modified multilayer perceptron neural network trained using the artificial bee colony based on risk sensitive loss function has impressive classification performance in terms of the overall accuracy as well as per class accuracy.