In this paper ACO (Ant Colony Optimization) algorithm, which is a well-known intelligent optimization method, is applied to selecting parameters for SVM.ACO has the characteristics of positive feedback, parallel mechanism and distributed computation. This paper gives comparison of ACO-SVM, PSO-SVM whose parameters are determined by particle swarm optimization algorithm, and traditional SVM whose parameters are decided through trial and error. The experimental results on real-world datasets show that this proposed method avoids randomness and subjectivity in the traditional SVM. Additionally it is able to gain better parameters which could dedicate to a higher classification accuracy than the PSO-SVM. Results confirm that proposed optimization method is better than the two others.