Gas-liquid two phase flow is probably the most important form of multiphase flows and is found widely in industrial applications, particularly in the oil and petrochemical industry. In this study, in the first instance a gas-liquid two phase flow test loop with both vertical and horizontal test tube was designed and constructed. Different volume fractions and flow regimes were generated using this test loop. The measuring system consists of a 137Cs single energy source which emits photons with 662 keV energy and two 1-inch NaI (Tl) scintillation detectors for recording the scattered and transmitted counts. The registered counts in the scattering detector were applied to the Multi-Layer Perceptron neural network as inputs. The output of the network was gas volume fraction which was predicted with the Mean Relative Error percentage of less than 0.9660%. Finally, the predicted volume fraction via neural network and the total count in transmission detector were chosen as inputs for another neural network with flow regime type as output. The flow regimes were identified with mean relative error percentage of less than 7.5%.
