Abstract The Cherenkov Telescope Array (CTA) will be the next major observatory for Very High Energy gamma-ray astronomy. Its optical throughput calibration relies on muon Cherenkov rings. This work is aimed at developing a fast and efficient muon tagger at the camera level for the CTA telescopes. A novel technique to tag muons using the capabilities of silicon photomultiplier Compact High-Energy Camera CHEC-S, one of the design options for the camera of the small size telescopes, has been developed, studying and comparing different algorithms such as circle fitting with the Taubin method, machine learning using a neural network and simple pixel counting. Their performance in terms of efficiency and computation speed was investigated using simulations with varying levels of night sky background light. The application of the best performing method to the large size telescope camera has also been studied, to improve the speed of the muon preselection.