The measure-correlate-predict process is a fundamental component of a wind resource assessment analysis. The generation of a long-term hindcast estimate of the wind resource is critical in order to take into consideration the natural inter-annual variability of the wind resource; however, this is a process which can introduce significant uncertainty into the resource assessment process. In this presentation several unique and advanced MCP processes are outlined and compared. In addition to traditional approaches, these include multivariate, non-linear, Fuzzy Logic, and distribution-matching methodologies. This presentation outlines advantages and limitations of these approaches and identifies criteria for validating MCP results and selecting an approach for use in the generation of a long-term wind turbine yield estimate. The incorporation of validated and site-appropriate MCP techniques can serve to significantly reduce error and uncertainty in wind resource assessment.