Based on debris-flow inventories and using a geographical information system, the susceptibility models presented here take into account fluvio-morphologic parameters, gathered for every first-order catchment. Data mining techniques on the morphometric parameters are used, to work out and test three different models. The first model is a logistic regression analysis based on weighting the parameters. The other two are classification trees, which are rather novel susceptibility models. These techniques enable gathering the necessary data to evaluate the performance of the models tested, with and without optimization. The analysis was performed in the Catalan Pyrenees and covered an area of more than 4,000 km2. Results related to the training dataset show that the optimized models performance lie within former reported range, in terms of AUC, although closer to the lowest end (near 70 %). When the models are applied to the test set, the quality of most results decreases. However, out of the three different models, logistic regression seems to offer the best prediction, as training and test sets results are very similar, in terms of performance. Trees are better at extracting laws from a training set, but validation through a test set gives results unacceptable for a prediction at regional scale. Although omitting parameters in geology or vegetation, fluvio-morphologic models based on data mining, can be used in the framework of a regional debris-flow susceptibility assessment in areas where only a digital elevation model is available.