This paper is devoted to proposing and testing a strategy for decomposing compound signals obtained in remote sensing applications through the automatic generation of cooperating ANNs that model it. Each ANN will specialize in one of the primitives that make up the whole. The evolutionary based algorithm that is proposed for this purpose implies that the combination of networks takes place at a phenotypic operational level, this is, the architecture of the networks is not the issue, but rather function they implement. This way, a population of networks that are automatically classified into different species depending on the performance of their phenotype, and individuals of each species cooperate forming a group to obtain a complex output, in this case the signal that is required. The magnitude that reflects the difference between ANNs is their affinity vector, which must be automatically created and modified depending on the actuation of the phenotype of each individual. The main objective of this approach is to model complex functions such as multidimensional signals, which are typical of remote sensing application, providing a decomposition of them into primitive functions.