In the biometrics community, challenge datasets are often released to determine the robustness of state-of-the- art algorithms to conditions that can confound recognition accuracy. In the context of automated human gait recognition, evaluation has predominantly been conducted on video data acquired in the active visible spectral band, although recent literature has explored recognition in the passive thermal band. The advent of sophisticated sensors has piqued interest in performing gait recognition in other spectral bands such as short-wave infrared (SWIR), due to their use in military-based tactical applications and the possibility of operating in nighttime environments. Further, in many operational scenarios, the environmental variables are not controlled, thereby posing several challenges to traditional recognition schemes. In this work, we discuss the possibility of performing gait recognition in the SWIR spectrum by first assembling a dataset, referred to as the WVU Outdoor SWIR Gait (WOSG) Dataset, and then evaluate the performance of three gait recognition algorithms on the dataset. The dataset consists of 155 subjects and represents gait information acquired under multiple walking paths in an uncontrolled, outdoor environment. Detailed experimental analysis suggests the benefits of distributing this new challenging dataset to the broader research community. In particular, the following observations were made: (a) the importance of SWIR imagery in acquiring data covertly for surveillance applications; (b) the difficulty in extracting human silhouettes in low-contrast SWIR imagery; (c) the impact of silhouette quality on overall recognition accuracy; (d) the possibility of matching gait sequences pertaining to different walking trajectories; and (e) the need for developing sophisticated gait recognition algorithms to handle data acquired in unconstrained environments.