Abstract. Solutions to the symbol grounding problem, in psychologically plausible cognitive models, have been based on hybrid connectionist/symbolic architectures, on robotic approaches and on connectionist only systems. This paper presents new simulations on the use of neural network architectures for the grounding of symbols on categories. In particular, the connectivity patterns between layers of the networks will be manipulated to scale up the performance of current connectionist models for the acquisition of higher-order categories via grounding transfer. 1 The Grounding of Symbols in Categories Cognitive models dealing with linguistic and symbol-manipulation tasks can use symbols that are either grounded or ungrounded (i.e. self-referential). Grounded symbols are those inherently significant to the cognitive system, such as an agent, and not mediated by the interpretation of an external user. Self-referential symbolic systems are those that use symbols that have no grounding in a...