Hyperparathyroidism (HPT) is a disorder in which the parathyroid glands produce too much parathyroid hormone (PTH), which may lead to hypocalcemic convulsions, cardiomyopathy, hypertension and other diseases, even threaten the lives of patients under certain severe conditions. Since HPT is usually multiple and ectopic with variable symptoms, the diagnosis and location of HPT is a difficult task even for senior radiologists. A transfer learning-based computer-aided diagnosis (CAD) approach is proposed for automated recognition of HPT in this paper. A dataset of the brightness-mode ultrasound images is developed for the HPT recognition, which is usually annotated by senior radiologists. We addressed the HPT recognition using the various computer vision algorithms on the HPT dataset and obtained good performances for all the algorithms. The experimental results demonstrated that the dataset is effective in aiding the diagnosis of HPT.