In this paper, a method for the identification of Hammerstein-Wiener systems is presented. The method extends the linear subspace intersection algorithm, mainly by introducing a kernel canonical correlation analysis (KCCA) to calculate the state as the intersection of past and future. The linear model and static nonlinearities are obtained from a regression problem using componentwise Least Squares Support Vector Machines (LS-SVMs).