Resonance inspection (RI), which employs the natural frequency spectra shift between the good and the anomalous part populations to detect defects, is a nondestructive evaluation (NDE) technique with many advantages, such as low inspection cost, high testing speed, and broad applicability to structures with complex geometry compared to other contemporary NDE methods. It has already been widely used in the automobile industry for quality inspections of safety critical parts. Unlike some conventionally used NDE methods, the current RI technology is unable to provide details, i.e., location, dimension, or types, of the flaws for the discrepant parts. Such limitation severely hinders its widespread applications and further development. In this study, an inverse RI algorithm based on maximum correlation function is proposed to quantify the location and size of flaws for a discrepant part. A dog-bone-shaped stainless steel sample with and without controlled flaws is used for algorithm development and validation. The results show that multiple flaws can be accurately pinpointed back, using the algorithms developed, and the prediction accuracy decreases with increasing flaw numbers and decreasing distance between flaws.