This thesis aims to understand if it is possible to apply machine learning techniques to predict the petrographical composition of a sample of soil given its mineralogical structure. The problem formalization requires predicting, from a set of continuous attributes (mineralogical constitution), a set of continuous features (petrographical constitution). The soil samples we have at disposal, for which both mineralogical and petrographical composition are known, present two challenges: their limited quantity and the non-uniform distribution of values of their features. These distributions are gaussian or exponential. We have addressed the prediction task with both regression and classification techniques. In the second case, we have discretized petrographical attributes using a custom methodology.