Gorgoniid octocorals constitute a diverse group of organisms that inhabit a wide range of marine environments. The group is currently defined by the presence of calcareous sclerites that are less than 0.3 mm in length with regularly arranged warts. Generic and specific classification schemes are based on the presence/absence of different sclerite classes in the sampled specimen as well as the frequency in which each class occurs in the sample. Sclerite classification typically has been difficult because a continuum of sclerite forms is found within and between species. Thus, the use of sclerites for phylogenetic inference and classification is problematic. Herein, we present a methodology to obtain quantitative measurements of large numbers of sclerites and used finite mixture modeling to assess the number of statistically different sclerite classes present in the eastern Pacific octocoral genus Pacifigorgia. We also test the ability of simple neural classifiers (perceptrons) to sort sclerites into the classes traditionally used in octocoral taxonomy. This methodology can be used for other gorgoniids and can be further extended to include shape quantifiers for groups other than those studied here.