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High efficiency and precision are the key requirements of modern miniature circuit breaker (MCB) manufacturing. Traditional manual assembly is inefficient and the quality is uneven. Conventional automatic assembling techniques with vibrating plate loading limit the flexibility in manufacturing. To solve the problems above and to meet future market demand, a flexible system using machine vision for miniature circuit breaker assembly is proposed. The system builds a visual recognition module that identifies the category, position and posture of an MCB component by the VGG-16 deep learning classifier and feature-template matching, and sends the recognition result to the industrial robot. which is guided to flexibly switch the corresponding robot jaw and perform different assembly motions. The experimental result shows that the visual system has an accuracy rate of 99.8% for category recognition, the coordinate deviation is within ± 0.3mm, and the rotation angle deviation is within ± 0.8 °, which meets the precision requirement of flexible MCB assembly.