Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproducing Kernel Hilbert Space (RKHS) theories, represent a promising approach to identify nonlinear systems via nonparametric estimation of the involved nonlinearities in a computationally and stochastically attractive way. However, application of LS-SVMs and other RKHS variants in the identification context is formulated as a regularized linear regression aiming at the minimization of the 2 loss of the prediction error. This formulation corresponds to the assumption of an auto-regressive noise structure, which is often found to be too restrictive in practical applications. In this paper, Instrumental Variable (IV) based estimation is integrated into the LS-SVM approach, providing, under minor conditions, consistent identification of nonlinear systems regarding the noise modeling error. It is shown how the cost function of the LS-SVM is modified to achieve an IV-based solution. Although, a practically well applicable choice of the instrumental variable is proposed for the derived approach, optimal choice of this instrument in terms of the estimates associated variance still remains to be an open problem. The effectiveness of the proposed IV based LS-SVM scheme is also demonstrated by a Monte Carlo study based simulation example. (C) 2015 Elsevier Ltd. All rights reserved.