Introduction. Price is the main factor that has a direct impact on the main results of the enter­prises, especially agricultural. The pricing policy is able to maintain the competitiveness of the enter­prise, to ensure its solvency and stability. Problem. Pricing is carried out in a complex interaction of a set of internal and external Factors. The corresponding complexity of the phenomenon leads to the need to include many Factors in the model, which can negatively affect the result. The numerous number of Factors necessitate the use of methods that allow the identification of the most statistically significant Factors and assess their relationship with the resulting feature. The aim of the article is to identify the main Factors and their influence on the formation and change of the price of agricultural products using Factor analysis. Methods. Different methods such as mathe­matical and statistical (factor analysis, principal component), economic and statistical (multiple regression method based on factor scores) and generalization were applied in the article. Results. The application of Factor analysis and method of principal components determine the most significant Factors influencing the formation of agricultural products prices. The author used different dimensionality reduction methods and obtained three main components: supply, demand, and the solvency of the po­pulation. This helped to reveal the relationship between variables and price without multicol­linearity problem. Conclusions. The author used Factor ana­lysis and the principal component method; it helped to take into account the peculiarities of the relationships between the indicators that characterize potato pricing, to eliminate multi­collinearity between independent indicators, to get fewer common Factors than the original number of variables, to obtain a quantitative assessment of the latent variable. The use of orthogonal rotation helped to distribute the load on all three Factors more evenly, which simp­lified their further interpretation and to save 95 % of the total primary information after rota­tion and to reduce the dimensionality of the fea­ture space, which is a high indicator for prac­tical tasks.