In many practical cases, a sensitivity analysis or an optimization of a complex time consuming computer code requires to build a fast running approximation of it - also called surrogate model. We consider in this paper the problem of building a surrogate model of a complex computer code which can be run at different levels of accuracy. The co-kriging based surrogate model is a promising tool to build such an approximation. The idea is to improve the surrogate model by using fast and less accurate versions of the code. We present here a new approach to perform a multi-fidelity co-kriging model which is based on a recursive formulation. The strength of this new method is that the co-kriging model is built through a series of independent kriging models. From them, some properties of classical kriging models can naturally be extended to the presented co-kriging model such as a fast cross-validation procedure. Moreover, based on a Bayes linear formulation, an extension of the universal kriging equations are provided for the co-kriging model. Finally, the proposed model has the advantage to reduce the computational complexity compared to the previous models. The multi-fidelity model is successfully applied to emulate a hydrodynamic simulator. This real example illustrates the efficiency of the recursive model.