The BirdCLEF challenge offers a large-scale proving ground for system-oriented evaluation of bird species identification based on au- dio recordings of their sounds. One of its strengths is that it uses data collected through Xeno-canto, the worldwide community of bird sound recordists. This ensures that BirdCLEF is close to the conditions of real- world application, in particular with regard to the number of species in the training set (1500). Two main scenarios are evaluated: (i) the identifi- cation of a particular bird species in a recording, and (ii), the recognition of all species vocalising in a long sequence (up to one hour) of raw sound- scapes that can contain tens of birds singing more or less simultaneously. This paper reports an overview of the systems developed by the six participating research groups, the methodology of the evaluation of their performance, and an analysis and discussion of the results obtained.