Deep learning systems such as Convolutional Neural Networks (CNNs) have shown remarkable efficiency in dealing with a variety of complex real life problems. To accelerate the execution of these heavy algorithms, a plethora of software implementations and hardware accelerators have been proposed. In a context of shrinking devices dimensions, reliability issues of CNN-hosting systems are under-explored. In this paper, we experimentally evaluate the inherent fault tolerance of CNNs by injecting errors within network modules, namely processing elements and memories. Our experiments demonstrate a non uniform sensitivity between different parts of the system. While CNNs are relatively resilient to errors occurring in processing elements, transient faults hitting memories lead to catastrophic degradation of accuracy.