Urgency of the research. The study of the methods of economic and financial evaluation of the effectiveness of development projects is an underdeveloped area of economic knowledge, due to the impossibility of predicting the obstacles associated with investments in the future, and the implementation of such projects is most often associated with risk and uncertainty. Target setting. Therefore, in this case, it is proposed to use fuzzy logic theory, which defines a modern approach to describe business processes that present uncertainty and inaccuracy of the source information. Actual scientific researches and issues analysis. The question of using the theory of fuzzy logic in the management of development projects is highlighted in the scholarly works of Ukrainian and foreign scholars such as Asai K, Borisov A. N., Gordienko I. V., Semenenko M. V., Mityushkin Yu. I., Mokin B. I. and others. Uninvestigated parts of general matters defining. Known studies have shown that classical control methods work quite effectively at fully deterministic control objects and environments, and for systems with incomplete information and high complexity, fuzzy analysis methods that are optimal to be adapted to a modern project management system for constructing an integrated neural network are optimal. The research objective. The task is to use the fuzzy models to move on to the development of modern management technology with the use of artificial neural networks to integrate the enterprise management system and development projects. The statement of basic materials. The transition from traditional control systems to systems with fuzzy logic occurs using fuzzy variables. Let's consider the process of neural network modeling in the integration of enterprise management systems and development projects for the construction of a single integrated enterprise management system. Conclusion. In this paper we propose a methodology for the implementation of investment projects for the implementation of information systems based on fuzzy-plural approach, which allows to take into account qualitative aspects that do not have an exact numerical evaluation.