The article provides an overview of geometric morphometrics, a statistical approach to analyzing form based on Cartesian landmark coordinates. It highlights the separation of shape from overall size, position, and orientation through Procrustes superimposition, which results in Procrustes shape coordinates. These coordinates can be used for statistical analysis, such as principal component analysis, multivariate regression, and partial least squares analysis, which can be visualized as actual shapes or shape deformations. The article discusses the concept of Kendall shape space, where shapes can be meaningfully compared using Euclidean distances, and the extension to form space by incorporating Centroid Size. It also covers the use of thin-plate splines for deformation grids and 3D visualizations, as well as the semilandmark algorithm for analyzing smooth curves and surfaces. The article emphasizes the exploratory nature of geometric morphometrics, allowing for the identification and quantification of previously unknown shape features. Additionally, it addresses issues such as asymmetry, missing data, and the availability of software tools for geometric morphometric analysis.The article provides an overview of geometric morphometrics, a statistical approach to analyzing form based on Cartesian landmark coordinates. It highlights the separation of shape from overall size, position, and orientation through Procrustes superimposition, which results in Procrustes shape coordinates. These coordinates can be used for statistical analysis, such as principal component analysis, multivariate regression, and partial least squares analysis, which can be visualized as actual shapes or shape deformations. The article discusses the concept of Kendall shape space, where shapes can be meaningfully compared using Euclidean distances, and the extension to form space by incorporating Centroid Size. It also covers the use of thin-plate splines for deformation grids and 3D visualizations, as well as the semilandmark algorithm for analyzing smooth curves and surfaces. The article emphasizes the exploratory nature of geometric morphometrics, allowing for the identification and quantification of previously unknown shape features. Additionally, it addresses issues such as asymmetry, missing data, and the availability of software tools for geometric morphometric analysis.