Accurate prediction of chemical composition of vacuum gas oil(VGO) is essential for the routine operation of refi neries. In this work,a new approach for auto-design of artificial neural networks(ANN) based on a genetic algorithm(GA) is developed for predicting VGO saturates. The number of neurons in the hidden layer,the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artif icial neural networks model are f ive physical properties,namely,average boiling point,density,molecular weight,viscosity and refractive index. It is verified that the genetic algorithm could fi nd the optimal structural parameters and training parameters of ANN. In addition,an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be effi ciently predicted. Compared with conventional artificial neural networks models,this approach can improve the prediction accuracy. Accurate prediction of chemical composition of vacuum gas oil (VGO) is essential for the routine operation of refi neries. In this work, a new approach for auto-design of artificial neural networks (ANN) based on a genetic algorithm (GA) is developed The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are f ive physical properties, namely, average boiling point, density It is verified that the genetic algorithm could fi nd the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy.