The artificial neural network (ANN) technique is applied to the remote sensing of the rms height and the correlation distance of one-dimensional rough surfaces. The surface is illuminated by a beam wave, and the intensity correlations of the scattered wave at two wavelengths in the specular and backward directions are used to determine the roughness parameters. Scattered intensity correlations calculated by Monte Carlo simulations are used to train the ANN, and two methods, the explicit inversion method and the iterative constrained inversion method, are used to perform the inversion. The technique is applicable to the range of parameters, 0.2 <σ/λ <1.0 and 1.0 < ℓ/λ < 5.0, where σ is the rms height and ℓ is the correlation distance of the surface roughness. An optimum surface area illuminated by the incident beam is approximately 20λ. Both the explicit inverse method and the iterative constrained inversion method give inversion values which are close to the target values. The iterative constrained inversion method appears to give smaller errors, although the required computer time is longer.