Principal Components Analysis (PCA) is often used to summarize high dimensional data.  The leading principal components are often used as descriptors of the principal modes of variation in the data, or interpreted as observable patterns.  In this talk I will discuss the role of PCA in dimension reduction, the interpretation of the leading eigenvectors and the possibilities of extending PCA to more complex types of data.