One of the greatest challenges in the characterization of bubbles in a bubble column has been the prediction of the bubble diameter and the gas holdup. In this study a novel technique for predicting the mean bubble diameter and the local gas holdup using a non-invasive ultrasonic method with neural network was investigated. The measurement parameters of the energy attenuation and the transmission time difference of ultrasound are used to obtain the mean bubble diameter and the local gas holdup in an air-water dispersion system using neural network reconstruction. Bubble size distributions in a 2-D bubble column are obtained experimentally by using a photographic method. An adequate selection of the neural network structure has been carried out to represent the training data. The representative results using the present structure show good agreement with the measured data.