It has been shown (Murray & Gold, 2004a) that the Bubbles paradigm for studying human perceptual identification can be formally analyzed and compared to reverse correlation methods when the underlying identification model is conceived as a linear amplifier (LAM). However the usefulness of a LAM for characterizing human perceptual identification mechanisms has subsequently been questioned (Gosselin & Schyns, 2004). In this article we show that a simple linear model that is formally analogous to the LAM--a linear perceptron trained with the delta rule--can make sense of several Bubbles experiments in the context of letter identification. Specifically, an analysis of input-output connection weights after training revealed that the most positive weights clustered around letter parts in a way that mimicked the diagnostic parts of letters revealed by the Bubbles technique (Fiset et al., 2008). Our results suggest that linear observer models are indeed unreasonably effective, at least as first approximations to human letter identification mechanisms.