A simple neural network model was used to predict binary solute adsorption onto granular activated carbon (GAG). While some data on binary adsorption were required, the neural network could be effectively trained using predominately single solute adsorption data, and only a limited number of data sets (<10) were necessary for effective performance. Once trained, the network was capable of predicting binary solute adsorptions even for systems showing nonideality.