This paper describes a hybrid feature selection algorithm that uses three different statistical measurements to evaluate features: between-class pairwised distance, linear separability and overlapped feature histogram. The paper presents detailed steps of each feature measurement. The hybrid feature selection algorithm applies the Bayesian EM (Expectation Maximization) to the features ranked by the three measurements alluded to above to select a sub-optimal feature set. The hybrid feature selection algorithm can be used as a preprocessing in a classification system and is independent of the classifier to be used in the subsequence stage. We have applied the hybrid feature selection algorithm to select vehicle signal features for fault diagnosis. Our experiments show that the hybrid algorithm provides a sub-optimal feature set that can be used to train a classifier to have very good generalization capability.