Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximumentropy estimate. It is the least biased estimate possible on the given information, i.e., it is maximally noncommittal with regard to missing information.The principle of maximum entropy, which has proven useful in other contexts, is adopted here to design a procedure for obtaining structural information from an incomplete set of diffraction data. A comparison is made between the proposed procedure and the conventional Fourier inversion method used in RDF analysis of non-crystalline materials. The maximum-entropy method is found to have a higher resolution and also the advantage of no adjustable parameters with a high degree of reliability.This method is iterative and uses more computer time than direct techniques; however, a number of comparative examples indicate that a significant improvement on the resultant structure in quality and resolution is possible with only a few iterations.