Early detection of tool defects enables proactive prevention of disruption, thus increasing productivity, maintaining quality and agility that brings significant competitive value to the organization. Hence, an effective tool wear monitoring system is vital for intelligent machining process. With the aim to develop an on-machine universal offline monitoring system, a novel quantitative image-based tool wear measurement system based on cross correlation analysis, is proposed to measure tool wear directly from the machining workbench. The sensitivity and accuracy of the proposed technique were further improved through cross-covariance analysis of original and worn tool images. Analyses on various wear conditions of drill bit, end mill, taper tap and carbide insert demonstrated the high effectiveness of the developed measurement system, reflected in the cross-correlation graphs pattern with wear measurement at a microscale down to 100 µm. The cross-correlation based measurement enables optimization of the machining productivity through just-in-time tool change through effective monitoring technique. © 2020 Elsevier Ltd