Current reservoir modeling strategies attempt to characterize the discrete fracture network (DFN) around producing wellbores to better predict both short-and long-term production levels and estimated ultimate recovery. A variety of data sources are used in describing the DFN, including image logs, petrophysical logs, geologically mapped fractures in the region (when available), and regional stress information. For hydraulic fracture stimulations, there is also microseismic data recorded during the stimulation of some wells. The event distribution obtained through microseismic monitoring gives a sense of where fracturing is occurring and how the stimulation progresses from the treatment zone into the reservoir. By using a multi-array distribution of sensors, seismic moment tensor inversion (SMTI) analysis may be performed for microseismic data, providing direct evidence of the DFN stimulated during completion activities. By performing this advanced analysis, a microseismic dataset includes the location, size, and orientation of stimulated fractures, allowing for detailed characterization of the DFN. This paper describes a methodology for characterizing a DFN observed through microseismic monitoring, which is illustrated by application to an example dataset from a North American shale play. By examining relationships between fractures and extracting statistical trends from the distribution of fractures, we arrive at a useful multifaceted description of the DFN which provides improved input data for reservoir modeling and allows a better understanding of the changes in the reservoir due to stimulation.