In this paper, the thermodynamic modeling of a vapor-compression cycle, based on a neural network approach is presented. A generalized radial basis function is used for the network, which takes previous control inputs and previous states as the network input and generates the predicted current state as the network output. The trained network is validated by non-trained data and shows all the process characteristics of a vapor-compression cycle for an air-to-water heat pump to a satisfactory degree.