In creating a pattern classifier, feature selection is often used to prune irrelevant and noisy features to producing effective features. Feature selection algorithms; try to classify an instance with lower dimension, instead of huge number of required features, with higher and acceptable accuracy. In fact an instance may contain useless features which might result to misclassification. An appropriate feature selection methods tries to increase the effect of significant features while ignores insignificant subset of features. In this paper, an efficient feature selection algorithm based on Cheetah optimization algorithm and support vector machine (CHOA-SVM) was used. First a population of cheetahs (feature subsets) were randomly generated, and then optimized by CHOA-SVM wrapper algorithms; finally the best fitness feature subset was applied to SVM classification. Experiments over a standard benchmark demonstrate that applying CHOA-SVM in the context of feature selection is a feasible approach and improves the classification results. The simulation experiment results have proved that the feature subset selection algorithm based on CHOA-SVM is very effective.