Advances in genomics and proteomics promise to transform biomarker research, in which the major challenges will not be the discovery of new markers but rather the optimal selection and validation of a subgroup of clinically useful markers from the large pool of candidates. Critically, the value of new biomarkers panels will need to be assessed in the context of readily available clinical information in order to create more actionable knowledge rather than just greater complexity. Appropriate methodologies for the clinical and statistical evaluation of so called "multi-marker strategies" have not been systematically defined. Although specific criteria for the appropriate clinical and statistical evaluation of multi-marker strategies will vary based on the intended use (e.g., diagnosis vs. screening), the ultimate measure of success is the ability for a biomarker panel to both correct a meaningful portion of misclassification by standard methods (discrimination) and to improve quantification of absolute risk (calibration) in comparison to existing clinical information. Findings should be validated in an independent dataset of the representative patient population before a given multi-marker strategy can be considered for clinical use. Here, we define multi-marker strategies, summarize recent examples of biomarker combinations in heart failure, address key statistical and clinical issues, and discuss future directions for this rapidly evolving field.