Dietary pattern analysis has recently received growing attention, as it might be more appropriate in studies of diet–disease associations than the single food or nutrient approach that has dominated past epidemiological research. Factor analysis is a technique which is commonly used to identify dietary patterns within study populations. However, the ability of factor solutions to account for variance of food and nutrient intake has so far remained unclear. The present study therefore explored the statistical properties of dietary patterns with regard to the explained variance. Food intake of 8975 men and 13 379 women, assessed by a food-frequency questionnaire, was aggregated into forty-seven separate food groups. Dietary patterns were identified by principal component analysis and subsequent varimax rotation. Seven factors were retained for both men and women, which accounted for about 31 % of the total variance. The explained variance was relatively high (>40 %) for cooked vegetables, sauce, meat, dessert, cake, bread other than wholemeal, raw vegetables, processed meat, high-fat cheese, butter and margarine. Factor scores were used to investigate associations between the factors and nutrient intake. The patterns accounted for relatively large proportions of variance of energy and macronutrient intake, but for less variance of alcohol and micronutrient intake, especially of retinol, β-carotene, vitamin E, Ca and ascorbic acid. In addition, factors were related to age, BMI, physical activity, education, smoking and vitamin and mineral supplement use.