The amount of electronic information has been growing fast, mainly due to the easiness of publication and spreading that Internet provides. Therefore, is necessary the organisation of information to facilitate its retrieval. Many works have solved this problem through the automatic text classification, associating to them several labels (multilabel classification). However, those works have transformed this problem into binary classification subproblems, considering there is not dependence among categories. Moreover, they have used thresholds, which are very sepecific of the classifier document base, and so, does not have great generalization capacity in the learning process. This thesis proposes two text classifiers based on the multinomial algorithm naive Bayes and its usage in an on-line text classification environment with user relevance feedback. In order to test the proposed algorithms efficiency, experiments have been performed on the Reuters 21578 news base, and on the Ohsumed medical document base.