Slot Grammar (SG) is a grammatical theory and parser used for source analysis in several of IBM's MT systems. SG provides a deeper level of analysis than many current parsers, and delivers both logical-level and surface-level analyses of sentences in single, unified parse structures. Parsing is driven largely by *slot frame* descriptions of word senses in the lexicon; slots simultaneously represent logical and syntactic roles of word sense arguments. We are developing SGs for several languages, and aiming for very broad coverage, with large slot frame lexicons and linguistically adequate rule sets. SG aims to capture linguistic universals as much as possible, so that rules can be shared across as many languages as possible. Some of the generalizations apply to all languages, and others apply to families of languages. We are writing a single grammar module for all the Latin-based languages (currently covering French, Spanish, and Italian) with switches for the differences. SG is largely a knowledge-based system (with human-coded linguistic rules); but it also employs numerical scoring heuristics for disambiguation, and corpus analysis statistics can feed into this scoring system. I will briefly describe the way SG analysis is used in our MT systems, and illustrate the advantages that it offers for MT.