The article describes the work on the creation of a neural network method for identifyinga person based on the mechanism of scanning and analyzing the pattern of palm veins as a biometricparameter. As part of the study, the prerequisites, goals and reasons for which the deve lopmentof a reliable biometric identification system is an important and relevant area of activityare described. A number of problems are formulated that are inherent in existing methods forsolving the problem: the graph method and the method based on calculating the distance expressedin various interval metrics. The description of the principles of their work is given.The tasks solved by personal identification systems are formulated: comparison of the subject ofidentification with its identifier, which uniquely identifies this subject in the information system.A mechanism for reading a pattern of veins from the palm of the hand, developed for analyzingan image obtained with a digital camera sensitive to infrared radiation, is described. When thepalm is in the frame, illuminated by the light of the near infrared range, the image obtainedfrom the camera becomes noticeable pattern of veins, vessels and capillaries that lie under theskin. Depending on the organization, the identification system may, based on the provided identifier,determine the appropriate access subject or verify that the same identifier belongs to theintended subject. Three methods for further analysis of biometric data and personal identificationare given: approaches based on categorical classification and binary classification, as wellas a combined approach, in which identification is first used by the first method, and then, bythe second, but already for a known access identifier defined on the first stage. The resultingarchitecture of the neural network for the categorical classification of the vein pattern is pr esented,a method for calculating the number of model parameters depending on the number ofregistered subjects is described. The main conclusions and experimental measurements of theaccuracy of the system when implementing various methods are presented, as well as diagrams ofchanges in the accuracy of models during training. The main advantages and disadvantages of theabove methods are revealed.