We propose a novel relevant feature selection technique which makes use of the slowness principle. The slowness principle holds that physical entities in real life are subject to slow and continuous changes. Therefore, to make sense of the world, highly erratic and fast-changing signals coming to our sensors must be processed in order to extract slow and more meaningful, high-level representations of the world. This principle has been successfully utilized in previous work of Wiskott and Sejnowski, in order to implement a biologically plausible vision architecture, which allows for robust object recognition. In this work, we propose that the same principle can be extended to distinguish relevant features in the classification of a high-dimensional space. We compare our initial results with state-of-the-art ReliefF feature selection method, as well a variant of Principle Component Analysis that has been modified for feature selection. To the best of our knowledge, this is the first application of the slowness principle for the sake of relevant feature selection or classification.