This thesis deals with multi-objet tracking and classification problem. It was shown that belieffunctions allow the results of classical Bayesian methods to be improved. In particular, a recentapproach dedicated to a single object classification which is extended to multi-object framework. Itwas shown that detected observations to known objects assignment is a fundamental issue in multiobjecttracking and classification solutions. New assignment solutions based on belief functionsare proposed in this thesis, they are shown to be more robust than the other credal solutions fromrecent literature. Finally, the issue of multi-sensor classification that requires a second phase ofassignment is addressed. In the latter case, two different multi-sensor architectures are proposed, aso-called centralized one and another said distributed. Many comparisons illustrate the importanceof this work, in both situations of constant and changing objects classes.