In autoimmune diseases, HEp-2 cells are used to detect antinuclear autoantibodies through Indirect Immunofluorescence (IIF) method. These cells can reveal different staining patterns that are relevant to diagnostic purposes. To classify them highly specialized personnel are required, who are not always available. In this respect, a medical demand is the development of a recognition system supporting such an activity. In this paper we present a hybrid Multi-Expert Systems (MES) based on the reduction of the multiclass learning task to several binary problems. The combination scheme, based on both classifier fusion and selection, employs reliability estimators that aim at improving the accuracy of final classification. The performance of such a hybrid system has been compared with those of a MES based only on classifier selection , showing that the hybrid approach benefits of advantages of both combination rules.