Abstract: An improved max-wins-voting(MWV) strategy for one-versus-one(OVO) classification is developed and the unclassifiable regions existing in conventional one are resolved.Firstly,using the decision functions obtained by training the SVM for classes ω_i and ω_j(j≠i,j=1,…,n),for class ω_i,a novel tuning function is defined in the range of 0~1.Secondly,the improved voting value for class ω_i equals to the traditional voting value plus the tuning function.Finally,a classification decision is made according to the improved voting value.For the data in the classifiable regions,the classification results using improved MWV strategy are the same as that using the traditional one.Whereas,the data in the unclassifiable region are determined by the tuning function.The comparison is done with experimental data in the application of fault diagnosis for gearbox.Experimental results demonstrate the superiority of the presented strategy.