The stability of the activities of organizations in the real sector of the economy at the macroeconomic level is the basis for creating the country's gross domestic product, developing technologies and strengthening the competitiveness of the country's products within the global economic system. To plan and forecast their activities, States and business participants are increasingly using modern methods of assessing the financial stability of companies. Today, approaches to building models for assessing the sustainability of companies are based mainly on econometric and statistical linear multidimensional methods of calculation, which does not allow us to identify hidden and nonlinear relationships that are inherent in the real world economy and the activities of economic entities. The article considers approaches to assessing the financial condition of organizations using neural network modeling and comparing it with previously used methods. In the course of the research, we developed a neural network for evaluating the financial condition of companies in the real sector of the economy, which allowed us to draw conclusions about the validity of this method of assessment in modern conditions. The article also highlights the key advantages and disadvantages of the neural network modeling method as a financial analysis tool. The scientific novelty of the article is to develop and evaluate a financial analysis tool that can be applied for practical purposes the economic entities and the substantiation of the complexity of neural network models in predicting bankruptcy; lack of methodological support; the need to develop special software; the duration of the learning process to achieve the required accuracy of the model; compliance with the requirement for equal proportions of the studied groups of objects; the correct choice of neural network architecture for research purpose; representativeness and consistency of source data.