Feature selection (FS) is a dimensionality reduction technique that aims to select a subset of the original features of a dataset which offer the most useful information. The benefits of feature selection include improved data visualisation, transparency, reduction in training and utilisation times and improved prediction performance. Methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the process with much success. This paper presents a novel fuzzy-rough FS technique which is guided by fuzzy entropy. The use of this measure in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS alone, with little loss or even an increase in overall classification accuracy.