The identification of different meat cuts for labelling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7987 meats cuts extracted from Semimembranosus muscles (i.e., Topside muscle), post-editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions); precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network (CNN) and residual network (ResNET). The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species.