The generalization from two-class classification to multiclass classification is not straightforward for discriminants which are not based on density estimation. Simple combining methods use voting, but this has the drawback of inconsequent labelings and ties. More advanced methods map the discriminant outputs to approximate posterior probability estimates and combine these, while other methods use error-correcting output codes. In this paper we want to show the possibilities of simple generalizations of the two-class classification, using voting and combinations of approximate posterior probabilities.