<p>The premature failure of wind turbines due to unknown loads leads to a reduction in competitiveness compared to other energy sources. Especially the failure of main bearings results in high costs and downtimes, as for an exchange of this component the rotor needs to be demounted. Load monitoring systems can make a significant contribution to understand and prevent such failures. However, most load monitoring systems do not take into account the main bearing loads in particular as there is no commercially applicable measuring system for this purpose. This work shows how main bearing loads can be estimated using virtual sensors. For this purpose, several regression models are trained with test bench data considering strain and displacement signals. It is investigated with which combination of signal type and regression model the highest accuracy is achieved. The results show that for either using strain or displacement signals an appropriate accuracy can be achieved. In particular, it is shown that a linear regression with interactions already achieves good accuracy and that further increases in regression model complexity do not add significant value.</p>