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The study of multiple classifier systems has become an area of intensive research in pattern recognition. Also in handwriting, recognition, systems combining several classifiers have been investigated. In the paper new methods for the creation of classifier ensembles based on feature selection algorithms are introduced. These new methods are evaluated and compared to existing approaches in the context of handwritten word recognition, using a hidden Markov model recognizer as basic classifier.