ABSTRACT The present doctoral thesis has as main purpose the study of heterogeneous catalysts for manufacturing fine chemicals. Thanks to the use of advanced combinatorial strategies, together with High-Throughput equipments and techniques to deeply characterize the materials, it has been possible to develop and optimize new active and selective catalysts for different interesting industrial processes. The study of catalysts based on supported Pd for cross-coupling reactions has allowed analyzing the influence of the most important variables during the materials synthesis, including the effect of the type of support, the introduction of different metal promoters, and the intensity of the selected thermal treatment. On the other hand, a series of micro and mesoporous titano-silicates have been optimized as catalysts for epoxidation reactions. The use of advanced combinatorial techniques, such as neural networks and genetic algorithms, coupled with a fundamental characterization of the catalysts, has allowed designing highly active and selective materials for epoxidizing heavy olefins with high industrial interest, such as methyl oleate. Finally, heterogeneous metal catalysts with unique properties for chemoselectively hydrogenating nitro groups in the presence of other reducible functionalities have been developed. The proposed approach offers an efficient and sustainable alternative for the manufacture of substituted anilines and oximes. Moreover, deep studies on the fundamental principles around the catalysis of metal nanoparticles during these hydrogenation processes have been carried out.