Assuming that a smoothness condition and a suitable restriction on the structure of the regression function hold, least squares estimates based on multilayer feedforward neural networks are able to circumvent the curse of dimensionality in nonparametric regression. The proof is based on approximation results concerning multilayer feedforward neural networks with bounded weights and a bounded number of hidden neurons. Finally, a brief outlook on continuing results is given.