Nowadays, much of the accumulated knowledge is stored as data. In many classification problems the relationship between a set of variables (attributes) and a target variable of interest must be learned. Among the tools capable of modeling real systems, Fuzzy Inference Systems are considered excellent with respect to the knowledge representation in a comprehensible way, as they are based on inference rules. This is relevant in applications where a black box model does not suffice. This model may attain good accuracy, but does not explain how results are obtained. This dissertation presents the development of a Fuzzy Inference System in an automatic manner, where the rule base should favour linguistic interpretability and at the same time provide good accuracy. In this sense, this work proposes the AutoFIS-Class model, an automatic method for generating Fuzzy Inference Systems for classification problems. Its main features are: (i) generation of premises to ensure minimum, quality criteria, (ii) association of each rule premise to the most compatible consequent term; and (iii) aggregation of rules for each class through operator that weigh the relevance of each rule. The proposed model was evaluated for 45 datasets and their results were compared to existing models based on Evolutionary Algorithms. Results show that the proposed Fuzzy Inference System is competitive, presenting good accuracy with a low number of rules.