In this paper we treat question answering (QA) as a classification problem. Our motivation is to build systems for many languages without the need for highly tuned linguistic modules. Consequently, word tokens and web data are used extensively but no explicit linguistic knowledge is incorporated. A mathematical model for answer retrieval, answer classification and answer length prediction is derived. The TREC 2002 QA task is used for system development where 33% of questions are answered correctly. Performance is then evaluated on the factoid questions of the TREC 2003 QA task where 23% of questions were answered correctly, which would rank the system in the top 10 of contemporary QA systems on the same task.