  Unsteady flows contain information about the objects creating them. Aquatic
organisms offer intriguing paradigms for extracting flow information using
local sensory measurements. In contrast, classical methods for flow analysis
require global knowledge of the flow field. Here, we train neural networks to
classify flow patterns using local vorticity measurements. Specifically, we
consider vortex wakes behind an oscillating airfoil and we evaluate the
accuracy of the network in distinguishing between three wake types, 2S, 2P+2S
and 2P+4S. The network uncovers the salient features of each wake type.
