The back break resulting from bench blasting is one of the most important side effects of blasting in open pit mines. Ignoring this phenomenon causes various problems such as: difficulty in drilling in new benches, having boulder in the next blast, instability of final wall and finally economic disadvantages. Prediction of back break can be used to design an optimum blast pattern, preventing such effects. In this paper, artificial neural networks (ANN) are adopted to predict the back break associated with a blast design pattern. 33-set of data for training and 4-set of data for test from the field, Gol-e-Gohar iron ore mine, Kerman, Iran, were used. The results obtained confirm the capability of the network designed.
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