Abstract: This study presented an empirical study to model the cost of the energy for high speed hard turning. A set of experimental machining data to cut hard AISI 4340 steel was obtained with a different range of cutting speed, feed rate and depth of cut with negative rake angle. Regression models were developed by using Box-Behnken Design (BBD) as one of Respond Surface Methodology (RSM) collections. Neural network technique was deployed using MATLAB to predict the energy as a part of the artificial intelligent methods. The data collected was statistically analyzed using Analysis of Variance (ANOVA) technique. Second order energy prediction models were developed by using (RSM) then the measured data were used to train the neural network models. A comparison of neural network models with regression models is also carried out. Predictive Box-Behnken models are found to be capable of better predictions for energy within the range of the design boundary.