Pedotransfer functions (PTFs), which estimate soil hydraulic parameters from easy to measure soil properties, are an important data source for hydrologic modeling. Recently artificial neural networks (ANNs) have become the tool of choice in PTF development. Recent developments in machine learning methods include the growing research and application of the alternative data-driven method called Support Vector Machines (SVMs). Support Vector Machines have gained popularity in many traditionally ANN dominated fields. Using the SVM eliminates the local minimum issue - - the minimum found is always the global one. The objective of this work was to see whethers using the SVM to develop PTFs may have some advantages compared with the ANN. We have used the Soil Profiles Bank of Polish Mineral Soils that includes hydraulic properties for 806 soil samples taken from 290 soil profiles. This database was repeatedly randomly split into training and testing data sets, and both SVMs and ANNs were trained and tested for each split with bulk density, sand and clay as input variables, and water contents at 11 soil water potentials as the output variables. The PTF performance was evaluated by using the test datasets to compute the coefficient of determination, the root-mean-squared error, and the slope and the intercept of the linear regression "predicted vs. measured water contents." The three-parameter SVMs performed mostly better than or with the same accuracy as the eleven-parameter ANNs. The advantage of SVM was more pronounced at soil matric potentials where larger relative errors have been encountered and the correlation between predicted and measured soil water contents was lower. It is worthwhile to consider SVM as a tool to develop PTF.