Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we do not consider the information provided by the other rules that are also compatible (have also been fired) with this example. In this paper we analyze this problem and propose to use FRMs that combine the different rules that have been fired by a pattern. We describe the behaviour of a general reasoning method and analyze two kinds of models, the first one using all the fired rules and the second one using partial information due to the fact that the rules with a lower association degree are not considered.