Discrimination performance, measured by the limiting error probability, is considered from the point of view of feature discriminating power. For assessing the latter, a concept of feature informativeness is introduced. A threshold feature selection technique is considered. Selection is incorporated into the discriminant function by means of an inclusion–exclusion factor which eliminates the sets of features whose informativeness do not exceed a given threshold. An issue is how this selection procedure affects the error rate when sample based estimates are used in the discriminant function. This effect is evaluated in a growing dimension asymptotic framework. In particular, the increase of the moments of the discriminant function induced by the curse-of-dimensionality is shown together with the effect of the threshold-based feature selection. The asymptotic normality of the discriminant function, which makes it possible to express the overall error probability in a closed form and view it as a function of a given threshold of selection.