This paper presents a feature based on out-of-vocabulary word statistics that complements the information sources used in the decision by state-of-the-art spam filters. The experiments included freely available spam filters as reference, SpamAssassin, Bogofilter, SpamBayes and SpamProbe, as well as a Naive Bayes classifier. The results show that the decision based on the proposed feature improves the performance of all spam filters under study.