Abstract: Support Vector Machine(SVM) was applied to establish Pedo-transfer functions(PTFs) to predict the Soil Hydraulic Characteristic Parameters(SHCPs), which included the saturated soil hydraulic conductivity, saturated soil water content, residual soil water content, and the logistic form of van Genuchten model parameters. The input variables were set as different treatments selected from soil texture, soil bulk density and soil organic matter content. Results indicate that there is no obvious difference between predicted and observed values, and that SVM is suitable for SHCPs prediction. The predicted results of different input treatments indicate that the best prediction effect is from the treatment with all the input variables included soil texture, soil bulk density and soil organic matter content. But there is no obvious difference of the prediction effect among other treatments while the input variables is soil texture, or soil texture and bulk density, or soil texture and organic matter. Compared with other methodologies of PTFs, SVM perform better than multivariate stepwise regression model, but with similar prediction efficacy comparing with BP neural network model.