Quantitative methods for identifying holobaramins have yet to be introduced into the field of baraminology. In this report we examine some quantitative methods which may be applied to a variety of biological data to empirically estimate the identity of holobaramins. Organismal relationships are based on a measure of dissimilarity called baraminic distance. A set of diagnostic statistics is described that allows the researcher to assess the completeness, variation, resolving power, and associations within a data set. Bootstrapped dendrograms are constructed to identify clusters of organisms, which are subsequently evaluated for phylogenetic discontinuity by comparing baraminic distance variation, and by correlating sets of baraminic distances. Using this approach both related monobaraminic groups and unrelated apobaraminic groups can be identified. The described methods are illustrated using data from humans and nonhuman primates, a group assumed by baraminologists to be polybaraminic. We have found that baraminic distances based on hemoglobin amino acid sequences, 12S-rRNA sequences, and chromosomal data were largely ineffective for identifying the Human holobaramin. Baraminic distances based on ecological and morphological characters, however, were quite reliable for distinguishing humans from nonhuman primates.