Chapter 11 discussed one of the many methods available for simultaneously analyzing more than one data set. While powerful and useful (especially for unveiling favored state evolution pathways), the extended empirical orthogonal function (EEOF) procedure has some important limitations. Notably, because the state dimensions rapidly expand as state vectors are appended end-to-end, EEOF analysis may not always be numerically tractable. For analyzing two data sets, taking note of their cross-covariance but not explicitly of individual sets’ covariance, the singular value decomposition (SVD) method is the most natural. This chapter discusses SVD analysis of two fields. SVD analysis can be thought of as a generalization of EOF analysis to two data sets that are believed to be related.