Identification of cutting conditions simplifies monitoring of machining operations to estimate tool wear and detection of breakage. An analytical model is introduced to simulate micro-end-milling operations more accurately than the conventional models by considering the feed rate. A genetic algorithm-based cutting condition identification program was developed to estimate the entry and exit angles. In all the studied cases, the program estimated the entry and exit angles with less than 3% error in less than 20 generations. In 20 to 150 generations error was reduced to less than 1%. The proposed procedure was found accurate and efficient for on-line monitoring applications.