Haptic Modeling of textile has attracted significant interest over the last decade. In spite of extensive research, no generic system has been proposed. The previous work mainly assumes that textile has a 2D planar structure. They also require time-consuming measurement of textile properties in construction of the mechanical model. A novel approach for haptic modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure for the textile, and deploys computational intelligence to estimate the mechanical properties of textile. The approach is designed primarily for display of textile artifacts in museums. The haptic model is constructed by superimposing the mechanical model of textile over its geometrical model. Digital image processing is applied to the still image of textile to identify its pattern and structure through a fuzzy rule-base algorithm. The 3D geometric model of the artifact is automatically generated in VRML based on the identified pattern and structure obtained from the textile image. Selected mechanical properties of the textile are estimated by an artificial neural network; deploying the textile geometric characteristics and yarn properties as inputs. The estimated mechanical properties are then deployed in the construction of the textile mechanical model. The proposed system is introduced and the developed algorithms are described. The validation of method indicates the feasibility of the approach and its superiority to other haptic modeling algorithms.