Abstract: Aimed toward improve the accuracy of objectoriented classification using SPOT5 imagery, three multiclassifier combination methods, include voting rule, Bayesian mean and fuzzy fusion rule, were discussed and a new fusion approach named votingfuzzy rule was developed, which synthesized conservative voting rule and fuzzy fusion rule. Five classifiers include minimums distance, Mahalanobis distance, Bayes rule, fuzzy logic and support vector machine, were involved in the combination. The result indicated that the votingfuzzy rule had higher total accuracy and Kappa index than three other combination rules, and also the Bayes rule, the best single classifier in all classifiers; furthermore, it reduced the difference of producer accuracy between classes. However, the combination effect wasn’t as obvious as indicated in literatures. The reason might owe to the high correlativity in the outputs of five classifiers, for them shared with a sample set, and the protocol with twenty two classes. Thus classifiers shouldn’t be trained with a same training sample set, or might select difference object features in practical application.