By relying upon tools of statistical mechanics of spin glasses, in this talk I will focus on Hebbian neural networks interacting in an heteroassociative manner to show that the overall network as a whole shows computationally capabilities that are lost within a single neural network. In particular I will show how these networks naturally disentangle spurious states recovering the original patterns forming these mixtures, thus providing a novel way of performing challenging pattern recognition tasks. The theory will be developed in the standard random setting then applications will be performed on structured datasets as the harmonic melodies.