Stall is a type of flow instability in compressors that sets the low-flow limit for compressor operation. During the past few decades, efforts to develop a reliable stall warning system have had limited success. This paper focuses on the small nonlinear disturbances prior to deep surge and introduces a new approach to identify these disturbances using nonlinear feature extraction algorithms including: phase-reconstruction of time-series signals and evaluation of a parameter called approximate entropy. To the best of the authors’ knowledge, this is the first time approximate entropy has been used for stall warning, and thus, its definition and utility are presented in detail. The technique is applied to stall data sets from two different compressors: a high-speed centrifugal compressor that unexpectedly entered rotating stall during a speed transient and a multi-stage axial compressor with both modal- and spike-type stall inception. In both cases, nonlinear disturbances appear, in terms of spikes in approximate entropy, prior to surge. The presence of these pre-surge spikes indicates the potential of using the approximate entropy parameter for small disturbance detection and stall warning. The details of the nonlinear feature extraction algorithm, including guidelines for its application as well as results from applying the algorithm to rig-level data, are presented.