Spectral imaging can provide spectral information from which spectral radiance or reflectance can be recovered at each image pixel. Recovery algorithms lead to good spectral and colorimetric performance by directly transforming RGB digital counts to spectral reflectances, but his approach is sensitive to the size and composition of the training set. What we propose here is a supervised method to select the most appropriate samples from a training database to buld the transformation matrix relating digital counts to spectral reflectances. Thus, this approach is tested with real images.