In many application data are often imperfect, incomplete or more generally uncertain. Whereas imperfect data are often removed from samples or corrected into precise data, using their initial imperfect structures to learn classifiers remains a challenge. As data uncertainty can be expressed in many forms (missing data, fuzzy sets, probabilities), working within the belief function framework enables a large number of models. The E2M decision trees is a methodology that provide predictions from uncertain data modelled by belief functions. In this paper, the problem of rubber quality prediction is presented with a belief function modelling of some data uncertainties. Some resulting E2M decision trees are presented in order to improve the interpretation of the tree compared to standard decision trees.