Method for supervised and non-linear dimension reduction of a set of learning data originating from measurements of physical characteristics of an original space of the dimension N, where N ≥ 3 towards a metric representation space having a dimension less than or equal to N, the method being implemented in a data-processing system, characterised in that the method comprises a step of estimating the class of a digitised test datum, in the original metric space, each test datum membership to at least one class grouping a plurality of data, the estimation step comprising: - a step of projecting data of the learning set towards the metric representation space, the projection step involving determining the projection positions of the data of the learning set in the representation space which minimise or maximise a stress function E, the stress function E being the sum of local stresses Eij, each local stress Eij being related to two data i and j of the learning set of the original space, and being defined as being the product of a function F(d ij, d* ij) and a weighting function G(d ij, d * ij), the functions F and G depending on the distance d ij representing the dissimilarity considered between the data i and j in the original space and the distance d * ij designating the distance between the projections of the two data i and j in the representation space, the function F quantifying the difference between the distances d * ij and d ij, the function G further depending on the level of conjoint membership to a class C ij associated with the data i and j, the level of conjoint membership to a class C ij indicating whether any two data of the learning set belong to the same class: E ij = F d ij, d * ij × G d ij, d * ij; - a step of representing projections of the learning set data towards the metric representation space, using the projection positions; - a step of relating the test datum to the projections of the learning set data, - a step of determining the class of the projections of the learning set data which is related to the test datum, which provides the most probable class of the test datum. - a step of projecting data of the learning set towards the metric representation space, the projection step involving determining the projection positions of the data of the learning set in the representation space which minimise or maximise a stress function E, the stress function E being the sum of local stresses Eij, each local stress Eij being related to two data i and j of the learning set of the original space, and being defined as being the product of a function F(d ij, d* ij) and a weighting function G(d ij, d * ij), the functions F and G depending on the distance d ij representing the dissimilarity considered between the data i and j in the original space and the distance d * ij designating the distance between the projections of the two data i and j in the representation space, the function F quantifying the difference between the distances d * ij and d ij, the function G further depending on the level of conjoint membership to a class C ij associated with the data i and j, the level of conjoint membership to a class C ij indicating whether any two data of the learning set belong to the same class: E ij = F d ij, d * ij × G d ij, d * ij; - a step of representing projections of the learning set data towards the metric representation space, using the projection positions; - a step of relating the test datum to the projections of the learning set data, - a step of determining the class of the projections of the learning set data which is related to the test datum, which provides the most probable class of the test datum.