Functional data analysis is a growing research field and numerous works present a generalization of the classical statistical methods to function classification or regression. In this paper, we focus on the problem of using Support Vector Machines (SVMs) for curve discrimination. We recall that important theoretical results for SVMs apply in functional space and propose simple functional kernels that take advantage of the nature of the data. Those kernels are illustrated on a spectrometric real world benchmark.