Molecular weight distribution, which is characterized by its averages like number average (Mn) and weight average (Mw), is one of the important properties of polybutadiene rubber (PBR), and it is difficult to measure. The objective of this work is to develop models to predict Mn and Mw from readily available process variables. Neural networks that are capable of mapping highly complex and non-linear dependencies have been adapted to develop models for the Mn and Mw of PBR. The molecular weight distribution and its averages of PBR samples collected over a wide range of operating conditions were measured by the conventional Gel Permeable Chromatograph (GPC) method. Neural networks were trained with relevant data to predict Mn and Mw from process variables. The trained networks were found to generalize well when tested with new data. (c) 2005 Wiley Periodicals, Inc.