Vocalization is an important clue in recognizing monkeys' behaviors. Previous studies have shown that the frequencies, the types and the lengths of vocalizations reveal significant information about social interactions in a group of monkeys. In this work, we describe a corpus of monkey vocalizations, recorded from Oregon National Primate Research Center with the goal of developing automatic methods for recognizing social behaviors of individuals in groups. The constraints of the problem necessitated using tiny low-power recorders, mounted on their collars. The recordings from each monkeys' recorder nonetheless contains vocalizations from not only the monkey wearing the recorder but also its spatial neighbors. The devices recorded vocalizations for two consecutive days, 12 hours each day, from each monkey in the group. Like in sensor networks, low power recorders are unreliable and have sample loss over long durations. Furthermore, the recordings contain high-levels of background noise, including clanging of metal collars against cages and conversations of caretakers. These practical issues poses an interesting challenge in processing the recordings. In this paper, we investigate our automated approaches to process the data efficiently, detect the vocalizations and align the recordings from the same sessions.