Decision level fusion between classi¯ers plays an increasingly important role in present speaker ver- i¯cation technology, while on the other hand, it is not the more classi¯ers involved in the fusion, the better the sys- tem performs. This paper proposes the Vector angle min- imum (VAM) criteria for the classi¯er selection in score fusion of speaker veri¯cation system. The main idea is to study the directions of the score-vectors in score space, ¯nd the score-vectors which can form a minimum angle with the standard-vector by linear combination, and take the corresponding classi¯ers into the fusion. The experimen- tal results show that the VAM-based selection can reduce the needed number of classi¯ers obviously and enhance the system performance. When compared to the n-best crite- ria selection, the Equal error rate (EER) of the fused sys- tem is relatively 7.2% lower when the number of selected classi¯ers is 16.