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Since the tourism earnings became a major part of foreign exchange in many countries, the tourism market has become much more competitive. In addition, tourism has been highly valued recently all over the world, and consequently, tourism market has been developed very fast. After quick development, it gradually becomes a saturated marketplace. Previous studies indicated that maintaining existing customers costs less than exploring new customers. Repeat visitors can not only reduce the cost but gain the long-term profits for the destinations. Thus, a method that can accurately predict repeat visitation intention is greatly needed. In this paper, we apply Support Vector Machines (SVM) to predict the repeat visitation. To evaluate the effectiveness of SVM, data are collected from tourists who visited Sun Moon Lake, which is the most famous destination in Taiwan and is the only place in Taiwan among the 50 best places to visit in China. Factor analysis (FA) is also employed to reduce variables and combined FA/SVM results are compared with those predicted from SVM only. The experimental results show that SVM can provide high accuracy rates than FA/SVM. Also, some SVM ensemble techniques can give higher true negative rates.