Bench blasting is the most common method of rock excavation in quarries and surface mines. Blasting has some environmental impact such as ground vibration, airblast, dust and fumes and flyrock. One of the undesirable phenomena in the blasting operation is flyrock, which is a propelled rock fragment by explosive energy beyond the blast area. Prediction of flyrock distance and size of the thrown rocks is a remarkable step in reduction and controlling the blasting accidents in blasting operations. Different empirical models have been developed to predict flyrock distance. However, due to complex relationships between blasting parameters and flyrock phenomena, empirical methods cannot take into account all of the relevant parameters. Artificial neural networks have revealed as valid approaches to analyze geotechnical problems and are mainly able to cover the limitation of the existing approaches. This paper presents an approach based on artificial neural network to predict flyrock distance and size of the thrown rocks in blasting operations. The obtained results demonstrate that artificial neural approach is applicable to predict flyrock distance and size of the thrown rocks in blasting operations.