Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can be alleviated is by using ensembles of classifiers, where a variety of classifiers (either different types of classifiers or different instantiations of the same classifier) are pooled before a final classification decision is made. Intuitively, classifier ensembles allow the different needs of a difficult problem to be handled by classifiers suited to those particular needs. Mathematically, classifier ensembles provide an extra degree of freedom in the classical bias/variance tradeoff, allowing solutions that would be difficult (if not impossible) to reach with only a single classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we survey select applications of ensemble methods to problems that have historically been most representative of the difficulties in classification. In particular, we survey applications of ensemble methods to remote sensing, person recognition, one vs. all recognition, and medicine.