Micro milling is a versatile machining process owing to its capability to machine, by material removal, micro-sized components with complex geometrical features. However, micro milling tools wear quickly, being a key issue to determine tool wear condition in order to prevent excessive tool wear or a sudden tool breakage while machining, which would waste the workpiece. Due to the small size of micro milling tools, direct measurement of the worn tool is not possible. In order to overcome this drawback, this paper presents a new method based on digital image processing where image captures of the micro tool and subsequent analysis provides a valuable information to determine the progression of tool wear. Tool wear is measured in flank wear, crater wear and tool radius reduction. Different approaches are compared so as to determine the best option for every set of images. These methods are based on the use of morphological operations, k-means clustering and Otsu Multilevel algorithm. Results show a good performance with differences of 5% between predicted and actual worn area, which satisfies the industrial requirements. This procedure can be transferred to industrial environments and implemented in collaborative robots, increasing the level of automation and facilitating the decision-making process.