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In a closed world setting, classifiers are trained on examples from a number of classes and tested with unseen examples belonging to the same set of classes. However, in most real-world scenarios, a trained classifier is likely to come across novel examples that do not belong to any of the known classes. Such examples should ideally be categorized as belonging to an unknown class. The goal of an open set classifier is to anticipate and be ready to handle test examples of classes unseen during training. The classifier should be able to declare that a test example belongs to a class it does not know, and possi- bly, incorporate it into its knowledge as an example of a new class it has encoun- tered. There is some published research in open world image classification, but open set text classification remains mostly un- explored. In this paper, we investigate the suitability of Convolutional Neural Net- works (CNNs) for open set text classifi- cation. We find that CNNs are good fea- ture extractors and hence perform better than existing state-of-the-art open set clas- sifiers in smaller domains, although their open set classification abilities in general still need to be investigated.