The tokamak is currently the principal magnetic confinement system for controlled fusion research. In seeking to understand the physics of the high temperature plasma inside the tokamak, it is important to have detailed information on the spatial distribution of electron density. One technique for density measurement uses laser interferometry, which gives line-integral information along chords through the plasma. This requires an inversion procedure to extract spatially local density information. In this paper we make use of feedforward networks to extract local density profiles from the line-integral data obtained from the multichannel interferometer on the JET (Joint European Torus) tokamak. An important feature of our approach is the use of profile data from a second high resolution diagnostic system, called LIDAR, to train the network. The LIDAR system provides data at high spatial resolution but with a low repetition rate, and the ref ore has a complementary role to interferometry which operates at a high sampling rate but with much lower spatial resolution. Results show that the neural network is able to extract significantly more detailed profile information than the conventional Abel inversion method currently used on JET.