We present a new application of a signal processing technique called fuzzy clustering for automatic identification of various structures seen in EIT images. This technique gives for each pixel a probability of belonging to a particular class. By assigning each pixel to the class for which it has the greatest probability of belonging, we obtain image segmentations. In EIT 19.5 nm images we distinguish the Quiet Sun, Coronal Holes, and the Active Regions, whereas in EIT 30.4 nm we extract the plages and part of the network boundaries. We also show how a multiwavelength approach leads to an improved segmentation of the different coronal structures.