This paper presents an original neural network based solution to the heterogeneous radar track fusion problem. The neural network is used to decide which tracks issued from two distinct sensors correspond to the same target. Classical fusion methods, based on distance criteria or the Chi-square test, can only be used when the sensors are of the same type, i.e. when they provide the same type of information, and the measurement vector is of the same dimension. When that is not the case, these criteria are applied only on the information that are common to the sensors, resulting in a lost of informations. Our neural approach, based on the use of a Kohonen map, allows to compare heterogenous tracks, without such a lost of informations. A neural network associated with a given sensor, maps each track on a two dimensional Kohonen grid. Each neuron encodes a monosensor track; the neuron inputs are defined as the latest estimated positions of the track. At convergence, the fusion of two tracks is decided depending on the position of each monosensor track on the grids: the best matching of two neural maps is defined in such a way that the distance between two projected tracks (of two different sensors) is minimized. This matching problem is similar to the well-known assignment problem which can also be solved by means of a neural network. Some simulation results are presented, using two dimensional and three dimensional radar tracks.