Principal Component Analysis (PCA) is a statistical technique for simplifying a dataset by reducing dimensions. The following chapter gives a rough idea, how PCA can be used to visualize a geometric property of a single tetrahedron. It is a quantitative measure for the distortion of a tetrahedron compared to a regular one. The property of having different mesh densities when measured in different directions is basis for calling a regular tetrahedron isotropic and a distorted tetrahedron anisotropic. A visualization scheme is devolved which gives a quantitative feeling of the distortion of a tetrahedron.