For the automated analysis of metaphase chromosome images, the chromosomes on the images need to be segmented first. However, the segmentation results often contain several non-chromosome objects. Elimination of non-chromosome objects is essential in automated chromosome image analysis. This study aims to exclude non-chromosome objects from segmented chromosome candidates for further analysis. A feature-based method was developed to eliminate non-chromosome objects from metaphase chromosome images. In a metaphase chromosome image, the chromosome candidates were segmented by a threshold first. After segmenting the chromosome candidates, four classes of features, namely, area, density-based features, roughness-based features, and widths, of the segmented candidates were extracted to discriminate between chromosomes and non-chromosome objects. Seven classifiers were used and compared to combine the extracted features to perform classifications. The experimental results show the usefulness of the combination of extracted features in distinguishing between chromosomes and non-chromosome objects. The proposed method can effectively separate non-chromosome objects from chromosomes and could be used as the preprocessing procedure for chromosome image analysis.