In many industrial processes, the most desirable variables to control are measured infrequently off-line in a quality control laboratory. In these situations, use of advanced control or optimization techniques requires use of inferred measurements generated from correlations. For well-understood processes, the structure of the correlation as well as the choice of inputs may be known a priori. However, many industrial processes are too complex and the appropriate form of the correlation and choice of input measurements are not obvious. Here, process knowledge, operating experience, and statistical methods play an important role in development of correlations. This paper describes a systematic approach to the development of nonlinear correlations for inferential measurements using neural networks. A three-step procedure is proposed. The first step consists of data collection and preprocessing. Next, the process variables are subjected to simple statistical analyses to identify a subset of measurements to be used in the inferential scheme. The third step involves generation of the inferential scheme. We demonstrate the methodology by inferring the ASTM 95% endpoint of a petroleum product using data from a domestic US refinery.