Dietary pattern analysis, which reflects the complexity of dietary intake, has recently received considerable attention by nutritional epidemiologists. Two general approaches have been used to define these summary variables in observational studies. The so-called a posteriori approach builds on statistical exploratory methods, whereas the so-called a priori approach focuses on the construction of pattern variables that reflect hypothesis-oriented patterns based on available scientific evidence for specific diseases. Several studies, both observational and clinical, suggest that these measures of overall diet predict disease risk, and that its application might be especially valuable in the development of food-based dietary guidelines. In this review, we describe different patterning approaches and the available evidence regarding the relationships between dietary patterns and risk of hypertension, type 2 diabetes mellitus, and coronary heart disease.