Despite a long history of research, relationships within fossil and extant Sphenisciformes remain unclear. This is largely because most fossil species were described on the basis of either the tarsometatarsus or humerus. Neither of these elements is particularly phylogenetically informative, and the extent of intraspecific morphological variation also remains unknown. Herein we investigate a new approach – the use of artificial neural-net (ANN) technology – to determine whether either of these elements can be reliably used to identify extant species. The DAISY ANN system was able to recognise most species from either tarsometatarsal or humerus morphology, but its success rate improved when the species training sets were combined into generic groups, indicating the need for larger image libraries. Our preliminary results suggest that these elements can allow reliable identifications for most taxa, but that the tarsometatarsus is on the whole a better element for this purpose. These results also demonstrate the potential for artificial neural-net technology to address problems in avian taxonomy.