The framing and analysis of the chromosome karyogram, which requires specific cytogenetic knowledge, is most important in the cytogenetic part. To improve classification accuracy, we proposed an algorithm for the chromosome image reconstruction in the image preprocessing part and the optimal pattern classification method using the hierarchical multilayer artificial neural network (HMANN) to classify the chromosome karyotype. We reconstructed chromosome images of twenty normal human chromosome by the image reconstruction algorithm. The four morphological and ten density feature parameters were extracted from the 920 reconstructed chromosome images. The feature parameters were composed to four combined feature parameters. The HMANN pattern classifier to classify chromosome images were composed to the HMANN 1 and the HMANN 2. The HMANN 1 was classified a chromosome group, and the HMANN 2 was classified an each chromosome number. Each combined feature parameters of the ten human chromosome images were used to learn HMANN and the rest of them were used to classify the chromosome images. According to the above experimental results, therefore, the Feature 4 has been shown very excellent in the classification experiment among Feature 1 through Feature 4.