Crying is one of the major means of infants to communicate with their surroundings, and is intended to point out any distress and attachment needs to their caregivers. Automatic detection of a baby cry in audio signals can be used for various purposes – from every-day applications to academic research. In this project we have developed a deep-learning based algorithm for automatic detection of baby cry in domestic audio recordings. The algorithm, based on a convolutional neural network (CNN), operates on log Mel-filter bank representation of the recordings. In order to give a better understanding of the advantages of CNN in identifying cry segments, we have also compared the results to a simple -NN algorithm, and a simple Artificial Neural Network (ANN) algorithm. The CNN based classifier yield considerably better results, producing 89% success in identifying baby cry segments.