This paper present a nonlinear system identification based kernel methods, such as regularization networks, support vector regression and kernel principal component analysis. In this case, black-box models are used in a particular space named reproducing kernel Hilbert space (RKHS) which only considered the input/output signals of the nonlinear system. In this particular space, the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. To prove the performances of the kernel methods, identification examples are illustrated with three single-input single-output (SISO) benchmark models.