We describe a new approach for dealing with hierarchical classification with a large number of classes. We build on Error Correcting Output Codes and propose two algorithms that learn compact, binary, low dimensional class codes from a similarity information between classes. This allows building classification algorithms that performs similarly or better than the standard and performing one-vs-all approach, with much lower inference complexity.