We introduce a novel Reservoir Computing (RC) approach for multi-dimensional temporal signals. Our proposal is based on routing the different dimensions of the driving input towards different dynamical sub-modules in a multi-reservoir architecture. At the same time, controllable interconnections among the sub-modules allow modeling the interplay between the different dynamics that might be required by the task. Experiments on synthetic and real-world time-series classification problems clearly show the advantages of the proposed approach in dealing with multi-dimensional signals in comparison to standard RC neural networks.