Nowadays, estimating the ampere consumption and achievement of the optimum condition from the perspective of energy consumption is one of the most important steps in reducing the production costs. In this research, we tried to develop an accurate model for estimating the ampere consumption using the artificial neural networks (ANN).In the first step, experimental studies were carried out on 7 carbonate rock samples in different conditions at particular feed rates (100, 200, 300 and 400) and depth of cut (15, 22, 30 and 35mm) using a fully instrumented laboratory rig that is able to change the machine parameters and to measure the ampere consumption. In the next step, a retro-propagation neural network was designed for modelling the sawing process to predict the ampere consumption. The input network consists of two parts: machine, work piece characteristics and the output of neural network was ampere consumption. This research evaluated the competencies of neural networks to estimate the ampere consumption in sawing process. The correlation coefficient between measured and predicted data in training and testing data is 0.95 and 0.97, respectively. The Root Mean Square Error (RMSE) for train and test data is 1.2 and 0.7, respectively. The results of this study show that the ANNs can be used to estimate the ampere consumption with high ability and low error for industrial applications. Moreover, the cost of sawing machine ampere consumption can be accurately estimated using this neural model from some important physical and mechanical properties of rock.