This project consists of investigating a technology to make possible the automatic mensuration of the ocular refraction errors (astigmatism, hypemietropia and myopia). For this, it intends to use recognition techniques and images analysis that will operate on images acquired recently by technique used for the information acquisition from the ocular globe, called Hartmann—Shack. The system should process an image supplied by the acquisition technique and, later on, to analyze it, extracting the necessary information for an automatic diagnosis of the possible existence of refractive errors in the examined ocular globe. The approach to be adopted uses techniques of artificial intelligence, more specifically, the use of neural networks. The huge progress in research and practical applications in that line in the last years certifies the precision and the efficiency of the same to analyze and to recognize external input data. It is believed that they can be used to identify an image from Hartrnann-Shack sensor that corresponds to an eye with anomaly, which anomaly type, as well as to quantify the magnitude anomaly. The purpose of using neural networks in the image analysis is to provide to the system with adaptability and robustness, that is, the system can respond (to measure) by understanding the data and not comparing the images as was accomplished previously. The use of intelligent software to evaluate the images also seeks to solve current problems of the difficulties that exist today for optimizing the system. Problems with small imperfections in the image acquisition or digitalization, in the preprocessing and segmentation of the images, can cause mistakes later on in the moment of the mensuration. This approach offers various possibilities in terms of academic research and ocular globe recognition. The possibility to recognize and to analyze the pertinent characteristics of the ocular globe allows to diagnose the refraction errors, and to obtain other information about the health of the ocular globe. Furthermore, a practical contribution of this approach is to suggest a new technology to measure the ocular refraction errors and the computational contribution based on analysis techniques and images recognition. Another contribution consists of the introduction of those techniques in a new field. It is possible that new application demands adaptations/new developments in the current processes, which can result in a contribution by this project for the computation area in addition to the immediate application of existent techniques. Finally, we believe the project provides a good opportunity for a closer approximation between the areas of Computer Science and Optometry.