The orange processing industry has grown vertiginously around the world recently once that orange derived products are embedded in a profitable market that constitutes a substantial part of the economy in many countries. Normally, orange classification in industries still is performed manually or using expensive technologies. Recent researches aimed to develop systems capable of executing this task considering elements of artificial intelligence to find ways of automating this process. This work aims to present the development of a low-cost oranges classification system through image processing and artificial neural networks concepts for classification and prediction of their main characteristics. Therefore, a systematic photographic and methodological procedure was applied for image processing and implementation of Hopfield recurrent artificial neural networks creating a trustworthy selection system. The results obtained achieved an acceptable average percentage of 85% for correct answers considering both criteria of quality and size, which means that the implemented system reaches similar or better results when compared to methods proposed in similar works. Additionally, an economic analysis indicated a favorable payback between 3 and 5 months, attesting the feasibility of its implementation. Overall, this work ensures an effective orange selection system with minimal human contact and low-cost for the orange industry.