Several real problem in computational modeling require function approximations. In some cases, the function to be evaluated in the computer is very complex, so it would be nice if this function could be substituted by a simpler and efficient one. To do so, the function f is sampled in a set of N pontos {x1, x2, . . . , xN}, where x(i) (is an element of) R(n), and then an estimate for the value of f in any other point is done by an interpolation method. An interpolation method is any procedure that takes a set of constraints and determines a nice function that satisfies such conditions. The Shepard interpolation method originally calculates the estimate of F(x) for some x (is an element of) R(n) as a weighted mean of the N sampled values of f. The weight for each sample xi is a function of the negative powers of the euclidian distances between the point x and xi. Kernels K : R(n) ×R(n) (IN) R are functions that correspond to an inner product on some Hilbert space F that contains the image of the points x and z by a function phi (the empty set) : R(n) (IN) F, i.e. k(x, z) =< phi (the empty set) (x), phi (the empty set) (z) >. In practice, the kernels represent implicitly the mapping phi (the empty set), i.e. it is more suitable to defines which kernel to use instead of which function phi (the empty set). This work proposes a simple modification on the Shepard interpolation method that is: to substitute the euclidian distance between the points x and xi by a distance between the image of these two point by phi (the empty set) in the Hilbert space F, which can be computed directly with the kernel k. Several tests show that such simple modification has better results when compared to the original method.