In this paper we compare three methods for selecting features that have recently been applied to the classification of defects on wood boards. A first method is based on statistical measures to determine how well features differentiate betweenclasses. A second method consist9 of leaving out each of the features in turn and performing classification on the remaining features. A third method is based on genetic algorithms. The performances of the three methods are measured on a databasecontaining color images of 900 pine wood defects classified into 9 categories. The best overall performance obtained was 93% of correct classifications on a test set, with only 20 out of 72 original features.
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