Nowadays, the miniaturization of many consumer products is extending the use of micro-machining operations in conventional shop floors with high quality requirements. However, the impact of cutting-tool wear on part dimensions, form and surface integrity is not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear and adapt the cutting conditions accordingly to minimize the production cost ensuring part specifications. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with an evolutionary optimization algorithm; and iv) a CNC interface module. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.