Abstract The study presents a data-driven framework for modelling parameters of hardfacing deposits by GMAW using neural models to estimate the influence of process parameters without the need of creating experimental samples of the material and detailed measurements. The process of GAS Metal Arc Welding (GMAW) hardfacing does sometimes create non-homogenous structures in the material not only in deposited material, but also in the heat-affected zone (HAZ) and base material. Those structures are not fully deterministic, so the modelling method should account for this unpredictable component and only learn the generic structure of the hardness of the resulting material. Artificial neural networks (ANN) were used to create a model of the process using only measured samples without any knowledge of equations governing the process. Robust learning was used to decrease the influence of outliers and noise in the measured data on the neural model performance. The proposed method relies on modification of the loss function and several of them are compared and evaluated as an attempt to construct general framework for analysing the hardness as a function of electric current and arc velocity. The proposed method can create robust models of the hardfacing layers deposition or other welding processes and predict the properties of resulting materials even for unseen parameters based on experimental data. This modelling framework is not typically used for metallurgy, and it requires further case studies to verify its generalisability.