Accurate segmentation of organs-at-risk (OAR) is essential for treatment planning of head and neck (HaN) cancers. A desire to shift from manual segmentation to automated processes allows for more efficient treatment planning. However, the technology of automated segmentation is hindered by complex and irregular morphology, poor soft tissue contrast, artifacts from dental fillings, variability of patient's anatomy, and inter-observer variability. In this study, we propose a state-of-the-art automated segmentation of OAR using a multi-output support vector regression (MSVR) machine learning algorithm to address these challenges under various selectable parameters. Shape image features were extracted using the histogram of oriented gradients and ground truth boundaries were obtained from physicians. Automated delineation of the OAR was performed on CT images from 56 subjects consisting of the brain stem, cochleae, esophagus, eye globes, larynx, lenses, lips, mandible, oral cavity, parotid glands, spinal cord, submandibular glands, and thyroid. Testing was done on previously unseen CT images. Model performance was evaluated using the dice similarity coefficient (DSC) and leave-one-subject- out strategy. Segmentation results varied from 66.9% DSC for the left cochlea to 93.8% DSC for the left eye globe. Analysis of the performance of a state-of-the-art algorithm reported in literature compared to MSVR demonstrated similar or superior performance on the segmentation of the OAR listed in this study. The proposed MSVR model accurately and efficiently segmented the OAR using a representative database of 56 HaN CT images. Thus, this model is an effective tool to aid physicians in reducing diagnostic and prognostic time.