Within a work environment, the cleanliness and organization of tools contribute greatly to the workers’ mental state and psychological well-being, allowing for more creativity and productivity in the workplace [1]. This is largely an issue within the industrial industry, where machines need to be built, maintained, and repaired. While being automated in larger factories, the tedious task of sorting hardware, such as screws, nuts, and bolts, is often performed manually in the context of smaller-scale factories and repair centers. However, the tediousness of this task often undermines the importance of the task and is often neglected or performed poorly. Existing industrial solutions to this issue are expensive and inflexible. This paper presents a low-cost, autonomous hardware sorting system that uses a custom-built Convolutional Neural Network (CNN) object detection model trained using TensorFlow and Keras. The system runs entirely on a Raspberry Pi 5 and uses a Microsoft Lifecam Studio webcam together with an H-bot gantry for mechanical sorting.