This paper describes a method developed to assist in the detection and reconstruction of a three dimensional (3D) model of the human upper airway using cone beam computed tomography (CBCT) image slices and a 3D Gaussian smoothing kernel. The segmented and reconstructed volumetric airway is characterized by the corresponding three principal axes that are selected for viewing direction orientation via rotation and translation. These axes are derived using the 3D Principal Component Analysis (PCA) result of the rendered volume. To finely adjust the view and access airway, the major and minor axes of each slice are also computed using the two dimensional (2D) PCA in the respective planes. The exterior volume view is visualized in two modes, namely, a solid surface (volume details transparent to user) view and a nontransparent (volume detail accessible) view. This functionality provides an application driven use of the 3D airway in CBCT based anatomy studies. The extracted information may be useful as an imaging biomarker in the diagnostic assessment of patients with upper airway respiratory conditions such as obstructive sleep apnea, allergic rhinitis, and other related diseases; as well as planning orthopedic/orthodontic therapies.