Automatic Identification of Gas Cylinder Storage Using Hopfield Neural Network RESUMEN Las companias que se dedican a la fabricacion, comercializacion y mantenimiento de cilindros para el gas licuado de petroleo en Colombia, estampan en placas de acero y soldadas al producto un codigo serial unico para ser identificado dentro del parque de cilindros del pais. Actualmente el proceso de identificacion es manual y se revisan aproximadamente 7000 cilindros al dia en una sola fabrica. El objetivo principal de este documento es presentar un sistema de vision artificial que utiliza redes neuronales artificiales para reconocer dicho codigo. Este sistema se compone fisicamente de un dispositivo portatil de ambiente controlado en iluminacion y escena para la adquisicion de las imagenes. Otro componente del sistema es el ajuste de la imagen. El ajuste se realiza a partir de filtros de mediana, binarizacion, etiqueta, y segmentacion; este procesamiento permite obtener mayor informacion significativa y discriminacion de la imagen. Por ultimo el componente inteligente para la identificacion se realiza con redes neuronales artificiales tipo Hopfield y un algoritmo que verifica la evolucion del reconocimiento de la imagen. La efectividad del sistema se reporta con los resultados experimentales obtenidos con base al error con una cantidad significativa de muestras. PALABRAS CLAVE: Vision artificial, cilindros de gas, codigos, seriales, redes neuronales artificiales, Hopfield. ABSTRACT Companies engaged in the manufacture, marketing and maintenance of cylinders for liquefied petroleum gas in Colombia, stamped steel plates and welded to the product a unique serial code to be identified within the cylinder of the country park. Currently, the identification process is manual and checked approximately 7000 cylinders per day in a single factory. The main objective of this paper is to present a vision system that uses artificial neural networks to recognize the code. This system consists physically of a portable device that controls light environment and scene for the acquisition of images. Another component of the system is to adjust the image. The adjustment is based on median filtering, binarization, label, and segmentation, this processing allows more meaningful information and image discrimination. Finally, the intelligent component identification is performed with Hopfield neural networks and an algorithm that checks the development of image recognition. The effectiveness of the system was reported with experimental results obtained on the basis of error with a significant number of samples. KEYWORDS: Artificial Vision, Gas Cylinders, Codes, Serials, Artificial Neural Networks, Hopfield.