Abstract: An algorithm for combining multiple classifiers is presented, which can find in the feature space the regions where each classifier has best performance. The correctly and incorrectly classified training samples from each classifier are clustered separately to form a partition of the feature space, and the performances of the classifier in each region are calculated. Then, the classifier responsible for the vicinity of the input sample is nominated to label the input pattern. The performance comparison between this algorithm and Kuncheva’s CS+DT method, as well as some simple aggregation methods, such as maximum, minimum, average, and majority vote using ELENA data sets, confirm the validity of the proposed combination scheme.