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We derive a new method for solving nonlinear blind source separation (BSS) problems by exploiting second-order statistics in a kernel induced feature space. This paper extends a new and efficient closed-form linear algorithm to the nonlinear domain using the kernel trick originally applied in support vector machines (SVMs). This technique could likewise be applied to other linear covariance-based source separation algorithms. Experiments on realistic nonlinear mixtures of speech signals, gas multisensor data, and visual disparity data illustrate the applicability of our approach.