The purpose of this article is to compare accuracy of activity type prediction models for accelerometers worn on the hip, wrists, and thigh. Forty-four adults performed sedentary, ambulatory, lifestyle, and exercise activities (14 total, 10 categories) for 3-10 minutes each in a 90-minute semi-structured laboratory protocol. Artificial neural networks (ANNs) were developed for four accelerometers (right hip, both wrists, and right thigh,) to predict individual activities and activity categories, with direct observation (DO) as criterion. The wrist-mounted accelerometers achieved the highest accuracy for individual activities (80.9%-81.1%) and activity categories (86.6%-86.7%); accuracy was not different between wrists. The hip-mounted accelerometer had the lowest accuracy (66.2% individual activities, 72.5% activity categories); thigh-mounted accelerometer accuracy (71.4% individual activities, 84.0% activity categories) fell between the wrist- and hip-mounted accelerometers. ANNs developed for accelerometers worn on the wrists and thigh provided high accuracy for activity type prediction and represent a potential approach to physical activity (PA) assessment.