In the field of isolated handwritten word recognition, the development of highly effective verification systems to reject words presenting ambiguities is still an active research topic. In this paper, a novel verification system based on support vector machine scoring and multiple reject class-dependent thresholds is presented. In essence, a set of support vector machines appended to a standard HMM-based recognition system provides class-dependent confidence measures employed by the verification mechanism to accept or reject the recognized hypotheses. Experimental results on RIMES database show that this approach outperforms other state-of-the-art approaches.