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The aim of Feature Subset Selection FSS algorithms is to select a subset of features from the original set of features that describes a data set according to some importance criterion. To accomplish this task, FSS removes irrelevant and/or redundant features, as they may decrease data quality and reduce several of the desired properties of classifiers induced by supervised learning algorithms. As learning the best subset of features is an NP-hard problem, FSS algorithms generally use heuristics to select subsets. Therefore, it is important to empirically evaluate the performance of these algorithms. However, this evaluation needs to be multicriteria, i.e., it should take into account several properties. This work describes a simple model we have proposed to evaluate FSS algorithms which considers two properties, namely the predictive performance of the classifier induced using the subset of features selected by different FSS algorithms, as well as the reduction in the number of features. Another multicriteria performance evaluation model based on rankings, which makes it possible to consider any number of properties is also presented. The models are illustrated by their application to four well known FSS algorithms and two versions of a new FSS algorithm we have developed.