Many solutions have been proposed to solve the multi-target tracking problem, using location informations provided by a radar sensor. The major drawbacks of classical methods are: (1) they need a priori knowledge on the problem, such as the measurement noise statistics, the number of targets, the probabilities of detection and false alarm; (2) they only treat one part of the problem, that is to say the plot/track association problem; (3) they are unable to solve the initiation problem, which is the key problem. We recently developed a neural solution for multiple radar target tracking, allowing to solve the problem in an integrated way, with few assumptions on the input data. This paper presents how measurement attributes, such as the Doppler velocity, the detection likelihood (classification probability of the detection) and the local report density (estimated on-line can be incorporated in our neural solution. Some simulation results are reported to compare the performances of our neural solution (with and without measurement attributes), with the joint probabilistic data association filter.