In the present paper, Data Mining techniques has been applied to evaluate the stability of slopes. For this propose, the R (www.r-project.org) software was used together with a user defined application developed at the University of Minho called RMiner. The factor of safety (FS) and probability of failure (PF) were computed for 365 homogeneous slopes using the software SLOPE/W with varying geometric parameters (e.g. height, height of the water surface and slope angle) and geotechnical parameters (weight density, cohesion intercept and angle of shearing resistance). Heights between 10 m and 15 m and slope angles between 40° and 70° were considered. This data allowed building a database to be analyzed using the Data Mining techniques. In this process, several algorithms were used for the prediction of FS and PF, such as multiple regression, regression trees, artificial neural networks, support vector machines and k-nearest neighbours. To evaluate the performance of each technique REC curves (Regression Error Characteristic) and several error measures were used. This application allowed developing reliable models to predict important safety parameters for slopes without a classical limit equilibrium calculation carried out. They also allow performing quick parametric studies for the early stages of slope design. To predict FS, the support vector machines showed the best overall performance. In the case of PF, the artificial neural network proved to be more reliable to predict this parameter. By this study, it was also possible to conclude that cohesion intercept was the parameter with more influence on the assessed safety parameters.