This article provides a feature ranking criterion for multi-class support vector machine classification. In the proposed criterion, feature effectiveness is estimated for individual features by their contributions to class separability in the kernel space. Class separability, measured by cosine similarity, is defined by an objective function that consists of within-class and between-class separabilities. Feature ranking is achieved for individual features by sorting their effectiveness scores. The proposed criterion is validated on University of California Irvine benchmark datasets and also applied to pitting diagnosis for a planetary gearbox. The experimental results demonstrate that the proposed criterion of feature ranking is computationally economic and effective.