Multiclass problems are usually of high technological value, but many classification methods are binary in origin. In the last years, several improved solutions based on the combination of simple classifiers were introduced. An interesting solution is based on creating a hierarchy of sub-problems by clustering prototypes of each one of the classes; there- fore the solution is heavily influenced by the label’s information. In this work we analyze a new strategy to solve multiclass problems that makes more use of spatial information than other methods. We construct a hier- archy of subproblems, but opposite to previous developments, based only on spatial information and not using a single prototype for each class. We evaluate the use of different clustering methods (either agglomera- tive or divisive) for this task and also the use two different classifiers (linear SVM and FDA–GenRidge) for each sub-problem (if needed, be- cause in several cases the clustering method directly gives a subset with samples of a single class). We compare the new method with several pre- vious approaches, finding promising results. The good performance of our approach is virtually independent of the classifier coupled to it, which suggest that it success is primarily related to the use of an appropriate clustering strategy.Sociedad Argentina de Informática e Investigación Operativa