This paper presents the effectiveness of convolutional neural network (CNN) to classify power quality problems. These problems arise mainly due to increase in use of non-linear loads, operation of devices like adjustable speed drives and power factor correction capacitors, which is a growing concern both for utilities and customers. This work uses the advances in supervised learning to classify different power quality time-series waveforms such as voltage sag, swell, interruption, harmonics, transients and flicker. CNN results in a very high classification accuracy compared to other traditional and machine learning methods in presence of noise. This process can be employed by utilities as well as customers to understand the cause and mitigate voltage quality problems.