We develop an operational convolutional neural network, that distinguishes 170 different South African birdsongs. The dataset consists of 3228 recordings extracted from Xeno-Canto, a user-generated database. Most recordings are however low quality, with multiple birdsongs a nd background noises. This problem is solved by pre-training using the larger and higher quality BirdCLEF2016 dataset (24607 recordings, containing however no South African birds). The fact that one makes a quite complicated recognition on the basis of a limited and noisy training set reveals a key ingredient about recognition and, “by a leap of faith”, about intelligence: the pre-existing architecture has to identify the relevant constitutive features.