A new method for automated feature selection is introduced. The application domain of this technique is fault diagnosis, where robust features are needed for modeling the wear level and therefore diagnosing it accurately. A robust feature in this field is one that exhibits a strong correlation with the wear level. The proposed method aims at selecting such robust features, while at the same time ascertain that they are as weakly correlated to each other as possible. The results of this technique on the extracted features for a real-world problem appear to be promising. It is possible to make use of the proposed technique for other feature selection applications, with minor adjustments to the original algorithm.