Feature selection is an essential topic in the field of pattern recognition. The feature selection strategy has a direct influence on the accuracy and processing time of pattern recognition applications. Features can be evaluated with either univariate approaches, which examine features individually, or multivariate approaches, which consider possible feature correlations and examine features as a group. Although univariate approaches do not take the correlation among features into consideration, they can provide the individual discriminatory power of the features, and they are also much faster than multivariate approaches. Since it is crucial to know which features are more or less informative in certain pattern recognition applications, univariate approaches are more useful in these cases. This paper therefore proposes subspace based separability measures to determine the individual discriminatory power of the features. These measures are then employed to sort and select features in a multi-class manner. The feature selection performances of the proposed measures are evaluated and compared with the univariate forms of classic separability measures (Divergence, Bhattacharyya, Transformed Divergence, and Jeffries-Matusita) on several datasets. The experimental results clearly indicate that the new measures yield comparable or even better performance than the classic ones in terms of classification accuracy and dimension reduction rate, (c) 2008 Elsevier Inc. All rights reserved.