In this paper we propose a new framework for feature selection consisting of an ensemble of filters for classification. Five filters, based on different metrics, were involved. Two different approaches of ensembles are presented by varying the role of the classification step. The different options to build an ensemble of filters were studied in detail, dealing with issues such as the presence of redundancy when joining the features selected by different methods. The adequacy of using an ensemble of filters instead of a single filter was demonstrated over a challenging scenario such as DNA microarray data.