A feature selection algorithm using quadratic programming is proposed based on feature margins. Firstly, the inner-class distance of features is taken as the coefficient of the quadratic terms in the objective function and the inter-class distance of features is used as the coefficient of the linear terms for searching informative features. The elements of the quadratic terms and the linear terms are normalized to balance the feature relation between inner class and inter-class. Then, the optimal solution vector is taken as the feature weight vector for selecting informative features. Finally, experiments on six different datasets show the effectiveness and feasibility of the proposed method.