A feature selection algorithm using Quadratic Programming is proposed in this paper based on feature margins. First, the inner-class distance of features is taken as the coefficients of the quadratic terms in the objective function and the inter-class distance of features is used as the coefficients 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. Experiments on 6 different datasets showed the effectiveness and feasibility of the proposed method.