Rock cuttability is expressed by specific energy (SE) that is defined as the energy required for cutting unit volume of rock. Direct determination of SE requires a rock cutting rig and is expensive and time-consuming. Therefore, empirical models have been alternative methods for predicting SE from rock properties. Two different predictive models of SE have been developed in this study using regression tree and artificial neural network (ANN) methods. Both empirical models employed the uniaxial compressive strength (UCS) and Mode I fracture toughness (KIC), being derived from tensile strength (t), as predictors. Data from four different studies have been used to develop the models. Statistical analyses on the data set have shown that both UCS and KIC are closely related to SE in a nonlinear form. Numerical and graphical measures of the goodness of the fit and ANOVA test have shown that regression tree and ANN models have performed similarly.