This study aims to develop a tool able to help decision makers to find the best strategie for slopes management tasks. It is known that one of the main challenges nowadays for every developed or countries undergoing development is to keep operational under all conditions their tranpostations infrastructure. However, considering the network extension and increased budget constraints such chalenge is even more difficult to accomplish. In the framework of transportations networks, particularly for railway, slopes are perhaps the element for which their failure can have a strongest impact at several levels. Therefore, it is important to develop tools able to help minimizing this situation. Aiming to achieve this goal, we take advantage of the high flexible learning capabilities of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have been used in the past to model complex nonlinear mappings. Both data mining algorithms were applied in the development of a classification tool able to identify the stability condition of a rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspections activities(visual information) to feed them. For that, two different strategies were followed: nominal classification and regression. Moreover, to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, SMOTE (Synthetic Minority Over-sampling Technique) and Oversampling. The achieved results are presented and discussed, comparing the performance of both algorithms (ANN and SVM) according to each modeling strategy as well as the effect of the sampling approaches. Also, a comparisson between both types of slopes is presented and discussed. An input-sensitivity analysis was applied allowing to measure the relative inlfuence of each model attribute.