This investigation develops an artificial neural network (ANN) algorithm to identify aeroelastic parameters of cable-supported bridge section models in smoothly flowing wind in a wind tunnel test. The ANN approach method uses observed dynamic responses to train a back-propagation (BP) neural network frame. The characteristic parameters of the section model for various wind velocities are estimated using weight matrices in the neural network. The eight flutter derivatives can then be determined precisely. The proposed method of identification is confirmed using numerical studies era stream section model. The procedure can also be applied to process experimental data obtained from wind tunnel tests involving fiat plate section models given various width/depth (B/D) ratios. Finally, the flutter characteristies of various bluff bodics are examined, as they are very sensitive to geometry and structural dynamics.