The use of artificial neural network based model for the on-line estimation of the Reid Vapor Pressure of stabilized gasoline in a stabilizer after the stripper-reabsorber in the fluid catalytic cracking unit is investigated. The quadratic basis function network (QBFN) which uses a simple quadratic function instead of sigmoid function typically used in back-propagation network is employed. 180 sets of historical operation data have been selected for training and testing the QBFN. To overcome the local minimum point which often occurs during the training phase, a new algorithm combining the simulated annealing approach with the improved GDR has been applied. Furthermore, the developed model has been installed on-line in a refinery for on-line testing. The testing results show that the model is sufficiently accurate and it can be used on site as an on-line analyzer.