According to the problem that the independence criterion based on the minimization of mutual information is
not normalized, a blind source separation(BSS) algorithm for post-nonlinear mixture(PNL) based on general correlation coefficient is introduced in this paper. Firstly, the PNL is taken as an indraft point to summarize this algorithm，which is the more practicable approximation to realism rather than linear model，meanwhile the independence criterion based on the generalized correlation coefficient is discussed. Then score function based on a Gram-Charlier expansion of densities is proposed. Finally, combined with the steepest descent method, the computations of regular matrix and parametric nonlinear mapping are given. The simulation results show that the proposed method is effective in BSS for the PNL and for the quantitative analysis of nonlinear correlation between variables.