Prediction and optimization of polymer properties is a complex and highly nonlinear problem with no easy method to predict polymer properties directly and accurately. The problem is especially complicated with high molecular weight polymers such as engineering plastics which have the greatest use in industry. The effect of modifying a monomer (polymer repeat unit) on polymerization and the resulting polymer properties is not easy to investigate experimentally given the large number of possible changes. This severely curtails the design of new polymers with specific end-use properties. In this paper, we show how properties of modified monomers can be predicted using neural networks. We utilize a database of polymer properties and employ a variety of networks ranging from backpropagation networks to unsupervised self-associating maps. We select particular networks that accurately predict specific polymer properties. These networks are classified into groups that range from those that provide quick training to those that provide excellent generalization. We also show how the available polymer database can be used to accurately predict and optimize polymer properties.