A dissertation submitted for the degree of Master of Science School of Computer Science University of the Witwatersrand. Johannesburg, 2015. / Rock art is the archaeological term used to describe pre-historic artworks placed on natural stone and as one of the earliest known traces of modern human creativity, it is a major component of world history and human heritage. Archival records and the art itself, however, are rapidly decaying requiring the need to preserve them for future generations and humanity as a whole. In line with this, the Rock Art Research Institute digitised their collections of photographs and historical records of the rock art in southern Africa. This has resulted in the South African Rock Art Digital Archive, a collection of over 275 000 images of paintings depicting a wide variety of objects such as humans and animals. The problem with this archive, however, is that most of the images are not labelled with the objects that appear in them. Manual labelling is infeasible due to the size of the archive but rock art researchers require this information to perform text-based search queries. Therefore, in this research, we present the combination of the Viola Jones object detection framework and a Support Vector Machine to automatically classify rock art objects. To test it, we have created and assessed the performance of classi ers for eland, elephant, human, and rhebuck rock art objects. We have performed the experiments using ve-fold cross-validation and found the results to be promising considering the variation and deterioration of the paintings.