The body condition of dairy cattle is an essential indicator of the energy supply of the animals. Various scoring systems are used in practice to quantify body condition. These systems rely on visual observation of different body parts and sometimes on collecting tactile data. In all cases, scoring requires expert knowledge and practice and is time-consuming. Therefore, it is rarely carried out on livestock farms. However, for animal husbandry and veterinary practice, it would be meaningful to have data on the condition of the animals continuously or even daily, which is not feasible with expert scoring. Researchers investigated how computer vision-based supervised deep learning, specifically neural networks, can automate body condition scoring. To execute this, they have used video recordings of the rumps of a large number of animals. The researchers have trained and tested various convolutional neural networks with this collected data. Scoring by trained networks yielded results that met or exceeded the agreement among experts.