This paper proposes a fuzzy neural network (FNN) based approach to construct an individual-oriented car-following system. The feature of this system is firstly to incorporate a personal risk-taking factor in addition to other mechanical factors as the input parameters. Through the learning capability of artificial network, the complex membership functions between the input factors and the output (i.e., the appropriate car-following headway) can be efficiently established, and then the fuzzy logic rules can be properly constructed. The performance of the FNN system is finally assessed against the field data. The results are inspiring that the system is proven capable of providing highly accurate predictions of the required car-following headways from person to person at various speeds. The success of this study provides some clues of utilizing FNN techniques in exploring some individual-oriented machines.