Principal component analysis (PCA) is a key method in chemometrics and other fields. This paper explains how to understand, use, and interpret PCA. It focuses on its application in chemometric areas but the results are generally applicable. The paper discusses the use of PCA in analyzing data from 44 red wine samples, measuring 14 parameters. PCA helps to condense variables into a smaller number of components that capture most of the variation in the data. The first principal component explains 25% of the variation, corresponding to approximately 3–4 variables. PCA is used to model data by finding linear combinations of variables that maximize variance. The first principal component is derived by maximizing the variance of the linear combination of variables, subject to the constraint that the weights are normalized. The variance of the component is measured and used to assess its explanatory power. PCA can be extended to multiple components, each capturing a portion of the variation. The number of components is determined based on the explained variation, using methods such as the scree test, eigenvalues below one, and the broken stick rule. PCA is also used for visualization and interpretation, with scores, loadings, and residuals providing insights into the data. Biplots combine scores and loadings to provide a comprehensive view of the data. PCA is a powerful tool for data analysis, providing a simplified view of complex data while preserving important information.Principal component analysis (PCA) is a key method in chemometrics and other fields. This paper explains how to understand, use, and interpret PCA. It focuses on its application in chemometric areas but the results are generally applicable. The paper discusses the use of PCA in analyzing data from 44 red wine samples, measuring 14 parameters. PCA helps to condense variables into a smaller number of components that capture most of the variation in the data. The first principal component explains 25% of the variation, corresponding to approximately 3–4 variables. PCA is used to model data by finding linear combinations of variables that maximize variance. The first principal component is derived by maximizing the variance of the linear combination of variables, subject to the constraint that the weights are normalized. The variance of the component is measured and used to assess its explanatory power. PCA can be extended to multiple components, each capturing a portion of the variation. The number of components is determined based on the explained variation, using methods such as the scree test, eigenvalues below one, and the broken stick rule. PCA is also used for visualization and interpretation, with scores, loadings, and residuals providing insights into the data. Biplots combine scores and loadings to provide a comprehensive view of the data. PCA is a powerful tool for data analysis, providing a simplified view of complex data while preserving important information.