Context: Issue reports are used to store the problems that occur using a software and them are submitted by developers and testers. After the software issue is detected then is redirect to an expert that operates in order to solve the problem. This operation is very time consuming, because in this process of creation of the bug report there is possible to find a wrong classification due to human misjudging. Goal: In this thesis’ work, we build a tool, that using machine learning techniques, to classify in an automatic way the issue report with the most probable label class. Method: This works is based over the Mozilla bugs stored in Bugzilla, a bug tracker for general purposes, and it is focused to the correct classification of a new bug.