The default pattern matching capabilities in today’s RDBMS are generally unable to cope with errors and variations that may exist in stored textual information. In this paper, we present SKIPPER, a simple search methodology that allows approximate string matching on multiple-attribute, large-scale customer address information for the Credit Collection industry. The proposed solution relies on the edit distance error model and the q-gram string filtering technique. We present an algorithm that integrates the methodology with existing RDBMS through SQL-based stored procedures.