Color flow imaging is a biomedical ultrasound modality used to visualize blood flow dynamics in the blood vessels, which are correlated with cardiovascular function and pathology. This is however done through a pulsed echo sensing mechanism and thus flow measurements can be corrupted by aliasing artefacts, hindering its application. While various methods have attempted to address these artefacts, there is still demand for a robust and flexible solution, particularly at the stage of identifying the aliased regions in the imaging view. In this paper, we investigate the application of convolutional neural networks to segment aliased regions in color flow images due to their strength in translation-invariant learning of complex features. Relevant ultrasound features including phase shifts, speckle images and optical flow were generated from ultrasound data obtained from anthropomorphic flow models. The investigated neural networks all showed strong performance in terms of precision, recall and intersection over union while revealing the important ultrasound features that improved detection. This study paves the way for sophisticated dealiasing algorithms in color flow imaging.