The traditional approaches—of symbolic artificial intelligence (AI) and of sub-symbolic neural networks—towards artificial cognition have not been very successful. The rule-based symbolic AI approach has proven to be brittle and unable to provide any real intelligence (Mckenna, Artificial intelligence and neural networks: steps toward principled integration, Academic Press, USA, 1994). On the other hand, traditional artificial neural networks have not been able to advance very much beyond pattern recognition and classification. This shortcoming has been credited to the inability of conventional artificial neural networks to handle syntax and symbols. Hybrid approaches that combine symbolic AI and sub-symbolic neural networks have been tried with results that fall short of the ultimate goal. It has been argued that traditional AI programs do not operate with meanings and consequently do not understand anything (Searle, Minds, brains & science, Penguin Books Ltd, London, 1984; Searle, The mystery of consciousness, Granta Books, London, 1997). It seems that in this way some essential ingredient is missing, but there may be a remedy available. Associative information processing principles may enable the utilization of meaning and the combined sub-symbolic/symbolic operation of neural networks.