Most NLP programs have failed to ‘scale-up’ because of the difficulty of developing large and robust lexical knowledge bases In our view, the development of tools for rapid prototyping and testing of domain-competent grammars has priority over the settlement of existing grammar theories In addition, the extensive acquisition of linguistic knowledge allows for a systematic, field evaluation of existing linguistic theories and demonstrates their adequacy to the purpose of automatic language processing. The important issue of semantic learning has been discussed in other papers (Velardi and Pazienza 1989): In this Paper, the focus is on the acquisition and testing of natural language grammars, to be used at different stages of natural language parsing, such as inflectional morphology, syntactic analysis, and sentence generation. The objectives of this research are: first, to render the activity of writing and customizing grammars better understood and time-con-stramed; secondly, to evaluate via extensive experimentation the practical limitations and advantages of Augmented Context Free Grammars (ACFG) in NLP; thirdly, to achieve uniformity in parsing. That one benefits from the view that developing grammars can be seen as manipulation of strings, without worrying about their content or associated content. The grammar development environment presented in this paper is a meta-analyzer, called META, that produces arbitrary semiotic relations between form and meaning. META is a flexible tool for string manipulation, independent of the morphologic, syntactic, or semantic interpretation of the attribute values synthesized by the production rules