An Artificial Neural Network (ANN) model has been applied to the prediction of key weld geometries produced using Gas Metal Arc Welding (GMAW) with alternating shielding gases. This is a recently developed method of supplying shielding gases to the weld area in which the gases are discretely supplied at a given frequency. The model can be used to predict the penetration, leg length and effective throat thickness for a given set of weld parameters and alternating shielding gas frequency. A comparison between the experimental and predicted geometries matched closely and demonstrates the effectiveness of this software approach in predicting weld outputs. The model has shown that the application of alternating shielding gases increases the penetration and effective throat thickness of a fillet weld whilst the leg length is reduced. A sensitivity analysis was performed which showed that the travel speed is the most influential input parameter when predicting weld geometries, this is to be expected for any given welding set-up due to the influence of the travel speed on the heat input. The sensitivity analysis also showed that the shielding gas configuration had the lowest influence on the output of the model. The output from the model has demonstrated that the use of alternating shielding gases during GMAW results in a step change in the weld metal geometry. This suggests that, in the case of alternating shielding gases, an increased travel speed is required to produce a similar weld geometry to that of the conventional Ar/20%CO2 technique.