This paper presents examples using bilinear interpolation and Artificial Neural Networks (ANNs) to approximate the underlying sonar model of a mobile robot in a real environment. Sonar samples were collected by a mobile robot from a real environment. The comparison between results of approximation by ANNs and interpolation show that ANNs has a better performance than bilinear interpolation for four different trials, especially for those low density sampling areas. This outcome indicates that the method of using ANNs to predict unknown data for simulation or prediction purposes is more useful than using interpolation not only for the accuracy but also for the convenience of collecting samples.