Artificial neural networks and the concept of group contribution are simultaneously used to correlate and predict the density of ionic liquids. Different topologies of a multilayer feed forward artificial neural network were studied and the optimum architecture was determined. Density data from the literature for 103 ionic liquids with 399 data points have been used for training the network. To discriminate among the different substances, the molecular mass and the structure of the molecule, defined by the concepts of the classical group contribution methods, were given as input variables. The capabilities of the designed network were tested by predicting densities for situations not considered during the training process of the network (82 density data points for 24 ionic liquids). The results demonstrate that the chosen network and the group contribution method employed are able to estimate the density of ionic liquids with acceptable accuracy for engineering calculations. The program codes and the necessary input files to calculate the density for other ionic liquids are provided.