Introduction: Craniofacial developmental morphology assessment is limited by a paucity of objective analytic techniques. Vector-ray analysis has recently demonstrated comprehensive quantitative data in craniosynostosis. Purpose: To develop a mathematical algorithm from volume craniofacial computed tomography (CT) to construct a three-dimensional cranial surface dataset that automatically derives objective craniofacial morphometric indices. To validate the algorithm against a known radiographic skull phantom and demonstrate the practical application of multiple vector-ray analysis in craniosynostosis. Materials and Methods: Using the graphical computational programming language, MATLAB, CT data was reformatted as orthogonal two-dimensional images (axial, coronal, sagittal planes). Anatomical landmarks (dorsum sella, glabella) define the coordinate system for index analysis. Traditional indices and distances from the dorsum sella to the outer table at specified angular increments in all dimensions are stored as spherical-coordinate rays and displayed graphically. Results: The cranial analysis tool can accurately and reproducibly measure distances as confirmed by numerical values in the data array and by graphic superimposition of measured data onto the CT dataset. Conclusion: In an automated fashion, the three-dimensional coordinate set can derive any desired morphometric measure, including traditional craniofacial indices and selected vector-ray analysis. Such measurements will provide a normative database and further objectively differentiate conditions of dysmorphology.