The classical stochastic discrete fracture network (DFN) approach is an attempt to simulate the statistical characteristics of fractures in a site, and does not need to match the observed fractures. In this study, the effects of conditioning the stochastically generated fractures with the fracture mapping and geophysical survey data on the equivalent properties of a fracture system were evaluated. A hypothetical real fracture system was assumed, and the classical and conditional stochastic approaches were applied to simulate it. Then the equivalent network properties such as equivalent permeability and density of the percolating cluster were calculated and compared to each other. The comparison results showed that conditioning with the fracture mapping data improved the classical stochastic approach in estimating the geometric connection by the fracture network while the performance of the conditional stochastic models estimating the hydraulic connection was similar to the classical stochastic model.