As multiple experts can confront and exchange their ideas in order to improve the decision-making process, a pattern recognition system can use several classifiers in order to improve its recognition rate. Moreover, various decisions strategies, implying these classifiers in different ways, can contribute to a same recognition task. A first strategy consists in deciding using different opinions: this is the combination of classifiers. A second strategy consists in using one or more opinions for better guiding other classifiers in their training stages, and/or to improve the decision-making of other classifiers in the classification stage: this is the cooperation of classifiers. The third and last strategy consists in giving more importance to one or more classifiers according to various criteria or situations: this implies the selection of classifiers. Since time could be a selection criterion, the temporal aspect of pattern recognition, i.e. the possible evolution of the classes to be recognized, can be treated by the selection strategy.