Global co-occurrence memory models, such as the Hyperspace Analogue to Language (or HAL), encode the symbols (words) in language input. These symbols form the basis of ultra high-dimensional word meaning vectors which have the characteristics of distributed representations: graceful degradation, concepts formed by a large array of elements, and straightforward generalization. An advantage of these models is that they use learning procedures that scale up to real world language problems. The HAL model has been used to investigate a wide range of cognitive phenomena (associative and semantic priming, semantic and grammatical categorization, connotative definitions, semantic judgements, parsing constraints, deep dyslexia, cerebral asymmetries, concept acquisition, and decision making) and some of these results will be presented. Recent work will be presented showing that the output of a global co-occurrence learning algorithm produces virtually the same output as a simple recurrent network (SRN) and that relatively few trials are required for learning. The global co-occurrence approach suggests a different view of similarity and has implications for the symbol-grounding problem, catastrophic interference, and the relationship between episodic associations and categorical knowledge. Real world applications of this model will be discussed and include the development of internet search tools and information retrieval filters.