The article discusses a method for forecasting the exchange rate. Artificial neural networks act as a forecasting tool. As a currency for the numerical testing of the proposed approach, the oil price in dollars, USD (value in rubles and tenge) was chosen as the most common currency in the world. The data will be processed from 2000 to 2019. In the course of the study, the indicators of the General exchange rate were identified with each other by day. When determining the dollar exchange rate using a single-layer neural network, the Adeline algorithm and the generalized Delta rule were used. Based on the prediction algorithm, the program code is written in Python. It is obvious that the quality of neural network training can be used to further predict the dynamics of the exchange rate.