Diffraction systematically causes error in acoustic measurements. Most probes are designed to reduce this phenomenon. On the contrary, this paper proposes a spherical probe a] lowing acoustic inten sity measurements in three dimensions to be made, which creates a diffracted field that is well-defined, thanks to analytic solution of diffraction phenomena. Six microphones are distributed on the surface of the sphere along three rectangular axes. Its measurement technique is not based on finite difference approximation, as is the case for the ID probe but on the analytic solution of diffraction phenomena. In fact, the success of sound source identification depends on the inverse models used to estimate inverse diffraction phenomena, which has nonlinear properties. In this paper, we propose the concept of nonlinear inverse diffraction modeling using a neural network and the idea of 3 dimensional sound source identification with better performances. A number of computer simulations are carried out in order to demonstrate the diffraction phenomena under various angles. Simulations for the inverse modeling of diffraction phenomena have been successfully conducted in showing the superiority of the neural network.