This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multidimensional category model is introduced for representing classes. In contrast with traditional flat and hierarchical category models; the multi-dimensional category model classifies each text document in a collection using multiple predefined sets of categories, where each set corresponds to a dimension. Since a multi-dimensional model can be converted to flat and hierarchical models, three classification strategies are possible, i.e., classifying directly based on the multi-dimensional model and classifying with the equivalent flat or hierarchical models. The efficiency of these three classifications is investigated on two data sets. Using k-NN, naïve Bayes and centroid-based classifiers, the experimental results show that the multi-dimensional-based and hierarchical-based classification performs better than the flat-based classifications.