Intelligibility and fidelity are the two key notions in machine translation system evaluation, but do not always provide enough information for system development. Detailed information about the type and number of errors of each type that a translation system makes is important for diagnosing the system, evaluating the translation approach, and allocating development resources. In this paper, we present a fine-grained machine translation evaluation framework that, in addition to the notions of intelligibility and fidelity, includes a typology of errors common in automatic translation, as well as several other properties of source and translated texts. The proposed framework is informative, sensitive, and relatively inexpensive to apply, to diagnose and quantify the types and likely sources of translation error. The proposed fine-grained framework has been used in two evaluation experiments on the LMT English-Spanish machine translation system, and has already suggested one important architectural improvement of the system.