The most important objective of blasting in open pit mines is rock fragmentation. Prediction of produced
boulders (oversized crushed rocks) is a key parameter in designing blast patterns. In this study, the
amount of boulder produced in blasting operations of Golegohar iron ore open pit mine, Iran was pre
dicted via multiple regression method and artificial neural networks. Results of 33 blasts in the mine
were collected for modeling. Input variables were: joints spacing, density and uniaxial compressive
strength of the intact rock, burden, spacing, stemming, bench height to burden ratio, and specific charge.
The dependent variable was ratio of boulder volume to pattern volume. Both techniques were successful
in predicting the ratio. In this study, the multiple regression method was superior with coefficient of
determination and root mean squared error values of 0.89 and 0.19, respectively.