The aim of the research was to create a prediction model for winter rapeseed yield. The constructed model enabled to perform simulation on 30 June, in the current year, immediately before harvesting. An artificial neural network with multilayer perceptron (MLP) topology was used to build the predictive model. The model was created on the basis of meteorological data (air temperature and atmospheric precipitation) and mineral fertilization data. The data were collected in the period 2008–2017 from 291 productive fields located in Poland, in the southern part of the Opole region. The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics. An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data. The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure, which was 9.43%. The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape. The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017 (designation by the T1-4_CY model).