A non–linear classification technique based on Fisher's discriminant is proposed. Main ingredient is the kernel trick which allows to efficiently compute the linear Fisher discriminant in feature space. The linear classification in feature space corresponds to a powerful non–linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach.