Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures. A principal component (PC) analysis was conducted based on these scans along with the manually measured body landmarks, and 100 PC scores were retained to account for 99% of the variance in the body shape and sitting postures. A PC-based fast fitting method was applied to estimate the occupant characteristics by fitting the SBSM to an incomplete depth image of a subject. The results demonstrate that a fast, inexpensive system can be used to produce useful estimates of occupant characteristics that could be applied to improve personalization of component adjustments, restraint systems, and infotainment systems.