The imagination of non-existent faces in random patterns, clouds and rock formations is known as facial pareidolia. We show that facial pareidolia also occurs naturally in a standard Convolutional Neural Network (CNN) trained on face recognition. For achieving this we propose a new method to analyse CNNs that combines feature visualisation and dimensionality reduction methods to cluster the hidden neuron activations in convolutional layers into groups with discriminative roles. The main contributions of the present paper are 1.) an approach that uses a CNN trained on human face detection for facial pareidolia simulation without any additional training on a target image set of abstract facial patterns and 2.) a novel way of improving the generalisation capacity of a CNN for cross-depiction recognition and domain adaptation scenarios using features learned by hidden neurons.