This paper explores the problem of feature subset selection for unsupervised learning within the wrapper framework. In particular, we examine feature subset selection wrapped around expectation-maximization EM clustering with order identi cation identifying the number of clusters in the data. We investigate two di erent performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood. When the true" number of clusters k is unknown, our experiments on simulated Gaussian data and real data sets show that incorporating the search for k within the feature selection procedure obtains better class" accuracy than xing k to be the number of classes. There are two reasons: 1 the true" number of Gaussian components is not necessarily equal to the number of classes and 2 clustering with di erent feature subsets can result in di erent numbers of true" clusters. Our empirical evaluation shows that feature selection reduces the number of features and improves clustering performance with respect to the chosen performance criteria.