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Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding the weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products that mitigate financial losses for farmers. In this study, an artificial neural network is set up and calibrated to a rich set of farm-level wheat yield data in Germany covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, serves as a benchmark. The empirical application reveals that the gain in estimation precision by using machine learning techniques compared with traditional estimation approaches is quite substantial and that the use of regionalized models and high-resolution weather data improve the performance of ANN.