Neural network models, which were initially used to model the processes of recognition of graphic images, are now widely used in the field of recognition of multi-parameter objects and in adjusting the parameters of complex systems. Such an application is effective in solving problems of information security management. With the help of neural networks, the tasks of classifying threats, choosing the parameters of protection measures, and adjusting the operating modes of information systems are successfully solved. In the scientific literature, there is a wide variety of approaches and methods for creating artificial neural networks. At the initial stage of designing information security systems, when it is necessary to determine the main parameters of the software being created, it poses a problem for the designer. Miscalculations made at this stage of the design can lead to the unsuccessful completion of the entire project. The article provides a brief comparative analysis of artificial neural networks that differ in the methods of setting (training) the network and in the form of its structure. The applicability, advantages, and disadvantages of such methods for tuning neural networks are characterized. These are the method of backpropagation of an error, genetic algorithm, iterative Widrow-Hoff algorithm with variable step, modified least squares method, and sequential learning method. The differences in the structure for networks designed to solve the problems of adjusting information processes and recognizing multi-parameter objects are shown. To regulate the processes, the structure of the neural network is studied on the example of the fuzzy neural network ANFIS. For the classification problems, the structure of a multilayer perceptron is given, in which the structure of the inner layers reflects the ontological network of the subject area under consideration. The presented results can be used to justify the type of neural network used for a specific task in the field of information security.