The Brazilian textile industry is an essential pillar of the country's economy, standing out globally as the fifth-largest textile hub and the fourth-largest in the clothing segment. However, one of the critical challenges faced by this sector is the reprocessing of fabrics, which leads to delivery delays, quality impacts, increased costs, and environmental impacts. Therefore, the aim of this study is to identify reprocessing in the dyeing process of a textile industry through preestablished patterns using a neural network. To achieve this goal, this research is being conducted in partnership with a company in the sector, focusing on data collection, preparation, processing, training and validating the neural network. Specifically, the focus is on the data collected from the production of polyamide, where approximately 95% of the reprocessing is classified as undefined, making the identification and precise resolution of these issues challenging. Thus, this research aims not only to enhance the efficiency of polyamide production but also to contribute to resource savings and compliance with environmental commitments, consolidating the concept of sustainability in the textile industry. The incorporation of artificial intelligence, such as neural networks, has emerged as an essential strategy to drive the textile industry toward more efficient and less impactful practices.