Determination of optimal machining parameters--spindle rate, feed rate, and depth of cut--has been a research topic for decades. Since the parameters of CNC machining significantly influence part machining time, part surface quality, and tool life, techniques of determining optimal machining parameters are in high demand in manufacturing industry. Usually the depth of cut and spindle rate are determined according to machinist manuals before machining; and the feed rate is determined subjectively either by CNC machine operators or programmers. As a result, the feed rate is not optimal in terms of the machining condition at every cutter location, and it is fixed at a conservative value causing longer machining time or shorter tool life. In this work, a generic and intelligent approach of feed rate determination for CNC profile milling is proposed. First a two-dimension chip load model is introduced and an example database of machining parameters is presented. Second a fuzzy rule-based system is established to predict the cutting force based on the radial and axial depths of cut, and the assumed feed rate. The next step is to identify the geometric features of the part, calculate the engagement angles of the geometric features, and find proper feed rates for them. Finally the approach is applied on an example part for profile milling, and the results are simulated with CATIA CAD/CAM system to demonstrate the advantages of this approach.