In chemical process modeling, the process neural network (PNN) usually consumes much time and falls into the local minima easily. In order to solve these problems, the extreme learning (EL) algorithm was used to train the PNN. Thus, an extreme learning-process neural network (EL-PNN) model was proposed. The outputs of the hidden layer of EL-PNN were obtained by the same means of PNN, and the weights connecting the hidden layer and output layer were then directly obtained by Moore-Penrose generalized inverse according to the EL algorithm. Meanwhile, to enhance the generalization performance of the EL-PNN, the structure risk was considered and a risk ratio parameter was introduced into the network. As a case study, the high-density polyethylene plant was selected to verify the effectiveness of the proposed model. The results show that the EL-PNN has a high learning speed and modeling precision, providing a new idea for process neural network in modeling complex chemical processes.