Parametric modeling techniques for spectrum analysis, based on the linear prediction principle, have previously been proposed to process NMR data. In this paper, they are tested on different practical NMR signals, and especially on in vivo 2D NMR spectroscopy data. The linear predicition version of the maximum entropy method, using AR modeling, and the Prony method are outlined with some considerations about the choice of the AR algorithm. Then simulation and experimental results obtained with the Prony method are presented and compared with those obtained with classical 2D Fourier transform processing. The data processed here result from homonuclear 2D J-resolved spectroscopy experiments performed to measure the spin-spin coupling constants between the three phosphorus nuclei of ATP in the rat brain. The parametric techniques (especially the Prony method) applied in both dimensions yield increased resolution and sensitivity and their ability to process limited data allows the total acquisition time to be reduced without loss of resolution. Although the noise may damage the performances, the results obtained here, on in vivo 2D data, are quite encouraging.