The problem of combining multiple feature rankings into a more robust ranking is investigated. A general framework for ensemble feature ranking is proposed, alongside four instan-tiations of this framework using different ranking aggregation methods. An empirical evaluation using 39 UCI datasets, three different learning algorithms and three different performance measures enable us to reach a compelling conclusion: ensemble feature ranking do improve the quality of feature rankings. Furthermore, one of the proposed methods was able to achieve results statistically significantly better than the others.