Phylogenetic analysis of protein sequences is widely used in protein function classification and delineation of subfamilies within larger families. In addition, the recent increase in the number of protein sequence entries with controlled vocabulary terms describing function (e.g. the Gene Ontology) suggests that it may be possible to overlay these terms onto phylogenetic trees to automatically locate functional divergence events in protein family evolution. Phylogenetic analysis of large datasets requires fast algorithms, and even fast, approximate distance matrix-based phylogenetic algorithms are slow on large datasets since they involve calculating maximum likelihood estimates of pairwise evolutionary distances. There have been many attempts to classify protein sequences on the family and subfamily level without reconstructing phylogenetic trees, but using hierarchical clustering with simpler distance measures, which also produce trees or dendrograms. How can these trees be compared in their ability to accurately classify protein sequences?