In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS-SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS-SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS-SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS-SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS-SVM model resulted in error reductions relative to that of the other models.Tensile strength was used for different geotechnical aims, and laboratory and full-scale studies were carried out to explore the possible relationship between fine production and water content of rock material [11]. Results of the laboratory work showed that the percentage of fines fraction produced by a cone crusher machine was a function of the type of rock tested and the tensile strength of individual specimens [11].There are mainly two methods for determining the tensile strength of rock materials. One of the methods is laboratory tests including direct uniaxial tensile test and indirect tensile tests (direct method) or making use of the previously derived empirical equations from literature called as indirect method. Direct uniaxial tensile test have some disadvantages and limitations. This test, which is theoretically the simplest and most effective method for the determination of tensile strength, is in fact difficult to carry out in practice for rock material and the difficulty in specimen preparation [4,9,[12][13][14][15][16]. Therefore, to determine tensile strength, indirect tensile tests are developed, for example Brazilian test, ring test, hoop test, bending test, etc. Brazilian test which is much more popular among those carried out has been proved as a satisfactory technique for determining the tensile strength of many rocks [6,[8][9][10][11][17][18][19][20]. Brazilian test measures tensile strength indirectly by developing tension across the diameter of a rock disc that is subjected to compression through a vertical load [8]. Testing proc...