This paper proposes the Parallel WiSARD Object Tracker (PWOT), a new object tracker based on the WiSARD weightless neural network that is robust against quantization errors. Object tracking in video is an important and challenging task in many applications. Difficulties can arise due to weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location frame by frame in real time. This paper proposes fast hybrid image segmentation (threshold and edge detection) in YcbCr color model and a parallel RAM based discriminator that improves efficiency when quantization errors occur. The original WiSARD training algorithm was changed to allow the tracking.