The Mexican Sign Language (MSL) is a language with its own syntax and lexicon. It is used by the deaf people, who use it to express thoughts, ideas and emotions. However, most of hearing people are unable to understand this language. The alphabet of any Sign Language (SL) is composed of signs where each sign corresponds to a letter of the alphabet of the dominant language in the region, for example, Spanish or English. Most signs of a signed alphabet are static, that means, they are only composed by the configuration of the hands. However, there are letters that are represented by signs that include movement. The present work proposes a system that, using artificial vision techniques and image processing, identify the 27 letters -including dynamic and static signs-of the Spanish alphabet in a mobile application. To solve the problem of sign identification it was used a combination of image processing techniques and deep learning. Canny and Camshift algortihms was implemented for the recognition of edges and trajectories in signs with movement. Once the characteristics were identified, the K-means and Tensorflow algorithms were used to classify the signs. The system achieves a 92% accuracy in the alphabet sign detection.