In recent years, visual attributes, which are mid-level representations that describe human-understandable aspects of objects and scenes, have become a popular topic of computer vision research. Visual attributes are being used in various tasks, including object recognition, people search, scene recognition, and many more. A critical step in attribute recognition is the extraction of low-level features, which encodes the local visual characteristics in images, and provides the representation used in the attribute prediction step. In this work, we explore the effects of utilizing different low-level features on learning visual attributes. In particular, we analyze the performance of various shape, color, texture and deep neural network features. Experiments have been carried out on four different datasets, which have been collected for different visual recognition tasks and extensive evaluations have been reported. Our results show that, while the supervised deep features are effective, using them in combination with low-level features can lead to significant improvements in attribute recognition performance. (C) 2016 Elsevier B.V. All rights reserved.