Linear approaches to low-dimensional feature extraction may not be appropriate when statistical data are generated by a nonlinear interaction of parameters. Equally inadequate are linear methods for determining the dimension of the feature space. This article estimates the intrinsic dimension of extracellular action potentials (EAPs), which can be viewed as the minimum number of nonlinearly interacting parameters sufficient to describe the data. When combined with nonlinear feature extraction methods, this information may lead to a more faithful, low-dimensional EAP representation. These points are demonstrated using EAPs recorded experimentally by a multisensor electrode.