Feature subset selection is one of the important problems in a number of fields namely data mining, machine learning, pattern recognition. It refers to the problem of opting for useful features that are neither irrelevant nor redundant. Since most of the data acquired through different sources are not in a proper shape to mine useful patterns from it therefore feature selection is applied over this data to filter out useless features. But since feature selection is a combinatorial optimization problem therefore exhaustively generating and evaluating all possible subsets is intractable in terms of computational cost, memory usage and processing time. Hence such a mechanism is required that intelligently searches for useful set of features in a polynomial time. In this study a feature subset selection algorithm based on conditional mutual information and ant colony optimization is proposed. The proposed method is a pure filter based feature subset selection technique that incurs less computational cost and proficient in terms of classification accuracy. Moreover, along with high accuracy it opts for less number of features. Extensive experimentation is performed based on thirteen benchmark datasets over a number of well known classification algorithms. Empirical results endorse efficiency and effectiveness of the proposed method.