The structural class of a protein domain can be approximately predicted according to its amino acid composition. However, can the prediction quality be improved by taking into account the coupling effect among different amino acid components? This question has evoked much controversy because completely different conclusions have been obtained by different investigators. To resolve such a perplexing problem, predictions by means of various algorithms were performed based on the SCOP database (Murzin et aL, 1995), which is more natural and reliable for the study of structural classes because it is based on evolutionary relationships and on the principles that govern their three-dimensional structure. The results obtained using both resubstitution and jackknife tests indicated that the overall rates of correct prediction by an algorithm incorporating the coupling effect among different amino acid components were significantly higher than those by the algorithms that did not include such an effect. A completely consistent conclusion was also obtained when tests were performed on two large independent testing datasets classified into four and seven structural classes, respectively. It is revealed through an analysis that the reasons for reaching the opposite conclusion are mainly due to (1) misclassifying structural classes according to a conceptually incorrect rule, (2) misapplying the component-coupled algorithm by ignoring some important factors and (3) misrepresenting structural classes with statistically insignificant training subsets. Clarification of these problems would be instructive for effectively using the prediction algorithm and correctly interpreting the results.