Automatic inspection of underwater pipelines has been a task of growing importance for the detection of a variety of events, which include inner coating exposure and presence of algae. Such inspections might benefit of machine learning techniques in order to accurately classify such occurrences. This article describes a deep convolutional neural network algorithm for the classification of underwater pipeline events. The neural network architecture and parameters that result in optimal classifier performance are selected. The convolutional neural network technique outperforms the perceptron algorithm, for different event classes, reaching on average 93.2% classification accuracy, whereas the accuracy achieved by the perceptron is 91.2%.