Nowadays, new security and protection systems for citizens are being developed, since criminals have found techniques to violate those already known, such as those based on fingerprints, facial recognition, iris and voice. Thus, using biometric data, new systems are being developed that are more secure, infallible and fast to identify each person, making it impossible to impersonate them, as has happened with other methods. Recently new identification methods have been proposed based on hand geometry and palmprint based on texture techniques for the identification of hand characteristics such as ridges, edges, points, and textures. Following this trend, this paper presents a method based on the detection of the palm print, acquired by contact, through the use of a scanner. For this purpose, the image is segmented to detect the silhouette of the hand and delimit the working area, achieving greater speed in identification. The images are then used as input to a convolutional neural network VGG 16 for learning and identification of subjects, achieving 100% accuracy.