The syntactic analysis component of a large-scale natural language query interface to relational data bases was greatly sped up by applying explanation-based learning(EBL), a machine learning technique. The idea is that one for most input queries can bypass the ordinary parser, instead using a set of learned rules. When no rule applies, one must pay the price of a small overhead. The set of learned rules is extracted automatically from sample queries posed by a user, thus tuning the system for that particular user. Several non-trivial problems, arising from the characteristics of the target system, were solved during the project. Measurements on a small test corpus indicated that the speed-ups when a learned rule could be used are on average a factor 30 and that the overhead, when no rule applied, is less than 3 percent.