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• It is possible to identify individual cows based on their morphological appearance. • A real-time computer vision system for automatic recognition of cows is presented. • Indications about quantity and characteristics of the images were assessed. Precision Livestock Farming relies on several technological approaches to acquire in the most efficient way precise and up-to-date data concerning individual animals. In dairy farming, particular attention is paid to the automatic cow detection and tracking, as such information is closely related to animal welfare and thus to possible health issues. Computer vision represents a suitable and promising method for this purpose. This paper describes the first step for the development of a computer vision system, based on deep learning, aiming to recognize in real-time the individual cows, detect their positions, actions and movements and record the time history outputs for each animal. Specifically, a neural network based on deep learning techniques has been trained and validated on a case study farm, for the automatic recognition of individual cows in videos recorded in the barn. Four cows were selected to train and validate a YOLO neural network able to recognize a cow starting from the coat pattern. Then, precision-recall curves of the identification of individual cows were elaborated for both the specific target classes and the whole dataset in order to assess the performances of the network. By means of data augmentation techniques, an enlarged dataset has been created and considered in order to improve the performance of the network and to provide indications to increase detection efficiency in those cases where data acquisition is not easy to be carried out for long periods. The mean average precision of the detection, ranging from 0.64 to 0.66, showed that it is possible to properly identify individual cows based on their morphological appearance and that the piebald spotting pattern of a cow's coat represents a clearly distinguishable object for a computer vision network. The results also led to obtain indications about the quantity and the characteristics of the images to be used for the network training in order to achieve efficient detections when facing with applications involving animals. [ABSTRACT FROM AUTHOR]