Reservoir simulation and prediction of production associated with hydraulic fracturing requires the input of the fracture geometry and the fracture properties such as the porosity and retained permeability. Various methods have been suggested and applied for deriving discrete fracture networks (DFN) from microseismic data as a framework for modeling reservoir performance. Although microseismic data is the best diagnostic for revealing the volume of rock fractured, its incompleteness in representing the deformation induced presents a challenge to calibrate and represent complex fracture networks created and connected during hydraulic fracture stimulation. We present an automated method to generate DFN models constrained by the microseismic locations and fracture plane orientations derived from moment tensor analysis. We use a Hough transform technique to find significant planar features from combinations of the microseismic source locations. We have modified the technique by using an equal-probability voting scheme to remove an inherent bias for horizontal planes. The voting mechanism is a general grid search in the space of fracture strike, dip and location (respectively) with grid cell sizes scaled by uncertainty estimates . We constrain fracture orientations with weighting based on the moment-tensor orientations of neighboring events and their associated uncertainties. Using two case studies, we demonstrate that our automated technique can reliably extract the complex fracture network based on good matches with the event cloud trends and the input moment tensor orientations. We also tested the sensitivity of the technique to event location uncertainty. With increasing location uncertainty, the details of the fracture network extracted are diminished with events grouping to larger scale features, but the general shape and orientation of the fracture network obtained is insensitive to the location uncertainty.