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A nickel-based aerospace superalloy, Inconel 718 presents machining challenges because of its hardness and strength. Monitoring and predicting chip morphology during milling is essential for early defect detection and process optimisation. This study examines the correlation between sensor signals with surface roughness and chip morphology in milling Inconel 718 using machine learning (ML). Due to progressive tool wear and heat generation, the surface roughness varies in addition to the chip exhibiting different morphologies, such as continuous, discontinuous, and oxidised chips. AE signals were analysed in the time and frequency domains to identify chip morphology transitions. An accelerometer captured cutting vibration signals that showed higher instability during discontinuous chip formation. Chip colour due to oxidization varies with milling forces as a result of tool wear. Based on multiple sensor data …