Abstract There are many scientifific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective. For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to illuminant spectrum selection for surface classifification. In the fifirst approach, a supervised framework, we formulate a biconvex optimization problem where we alternate between optimizing support vector classififier weights and optimal illuminants. In the second approach, an unsupervised dimensionality reduction, we describe and apply a new sparse Principal Component Analysis (PCA) algorithm. We effificiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classifification accuracy of a monochrome imaging sensor with optimized illuminants to the classifification accuracy of conventional RGB cameras with natural broadband illumination.