  Many searches for physics beyond the Standard Model at the Large Hadron
Collider (LHC) rely on top tagging algorithms, which discriminate between
boosted hadronic top quarks and the much more common jets initiated by light
quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively
takes a "digital image" of each jet, with pixel intensities given by energy
deposits in individual HCAL cells. Viewed in this way, top tagging becomes a
canonical pattern recognition problem. With this motivation, we present a novel
top tagging algorithm based on an Artificial Neural Network (ANN), one of the
most popular approaches to pattern recognition. The ANN is trained on a large
sample of boosted tops and light quark/gluon jets, and is then applied to
independent test samples. The ANN tagger demonstrated excellent performance in
a Monte Carlo study: for example, for jets with p_T in the 1100-1200 GeV range,
60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the
physical features of the jets identified by the ANN tagger as the most
important for classification, as well as correlations between the ANN tagger
and some of the familiar top-tagging observables and algorithms.
