Summary: In recent years residual neural networks (ResNets) as introduced by K. He et al. [“Deep residual learning for image recognition”, in: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, CVPR 2017. Los Alamitos, CA: IEEE Computer Society. 770–778 (2016; doi:10.1109/CVPR.2016.90)] have become very popular in a large number of applications, including in image classification and segmentation. They provide a new perspective in training very deep neural networks without suffering the vanishing gradient problem. In this article we show that ResNets are able to approximate solutions of Kolmogorov partial differential equations (PDEs) with constant diffusion and possibly nonlinear drift coefficients without suffering the curse of dimensionality, which is to say the number of parameters of the approximating ResNets grows at most polynomially in the reciprocal of the approximation accuracy \(\varepsilon > 0\) and the dimension of the considered PDE \(d\in \mathbb{N}\). We adapt a proof in A. Jentzen et al. [Commun. Math. Sci. 19, No. 5, 1167–1205 (2021; Zbl 1475.65157)] – who showed a similar result for feedforward neural networks (FNNs) – to ResNets. In contrast to FNNs, the Euler-Maruyama approximation structure of ResNets simplifies the construction of the approximating ResNets substantially. Moreover, contrary to [Jentzen et al., loc. cit.], in our proof using ResNets does not require the existence of an FNN representing the identity map, which enlarges the set of applicable activation functions.