This study describes the development of a novel stall-detection methodology for low-speed axial-flow fans. Because aerodynamic stall is a major potential cause of mechanical failure in axial fans, effective stall-detection techniques have had wide application for many years. However, aerodynamic stall does not always result in mechanical failure. A subsonic fan can sometimes operate at low speeds in an aerodynamically stalled condition without incurring mechanical failure. To differentiate between aerodynamic stall conditions that constitute a mechanical risk and those that do not, the stall-detection methodology in the present study utilizes a symmetrized dot pattern (SDP) technique that is capable of differentiating between stall conditions. This paper describes a stall-detections criterion based on a SDP visual waveform analysis and develops a stall-warning methodology based on that analysis. This study presents an analysis of measured acoustic and structural data across nine aerodynamic operating conditions represented in a matrix. The matrix is a combination of (i) three speeds (full-, half-, and quarter-speed) and (ii) three operational states (stable operation, incipient stall, and rotating stall). The matrix of SDPs and structural data are used to differentiate critical stall conditions (those that will lead to mechanical failure of the fan) from noncritical ones (those that will not result in mechanical failure), thus providing a basis for an intelligent stall-warning methodology.