Using (1) solubility, (2) molecular weight, and (3) density, a three-layer feed-forward neural network was constructed and tested to predict the IFT at the crystal/solution interface. The concentration of solute in liquid phase, (1) concentration of solute in solid phase, (4) temperature, (3) density and (2) molecular weight of crystal were used as inputs to predict the interfacial tension at the crystal/liquid interface (σ_SL). The network was trained using the solubility information for 28 systems to predict the σ_SL value and was validated with 29 new systems. Despite the limited number of data used for training, the neural network was capable of predicting σ_SL successfully for the new inputs, which are kept unaware during the training process. The σ_SL value that is predicted by the artificial neural network during the training and testing process was compared with σ_SL predicted from the widely used empirical expression. For most of the systems, ANN better predicts IFT.

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