This paper addresses the issue of robotic haptic exploration of 3D objects using an enhanced model of visual attention where the latter is applied to obtain a sequence of eye fixations on the surface of objects guiding the haptic exploratory procedure. According to psychological studies, somatosensory data resulted as a response to surface changes sensed by human skin is used in combination with kinesthetic cues from muscles and tendons to recognize object. Accordingly, a series of sequential tactile images are obtained for each object from various viewpoint during an exploration process. We take advantage of the contourlet transform to extract several features from each tactile image whose dimensionality is reduced to one using a Self Organizing Map. Through this process, a numerical sequence is obtained for each exploration. Similar sequences are then grouped in clusters whose labels are used as features for a classification algorithm. In addition to this somatosensory feature, other kinesthetic inputs including the probing locations and the angle of the sensor surface with respect to the object, in consecutive contacts are added as features and further used for the purpose of object recognition. The proposed framework is applied to a set of four virtual objects and a virtual force sensing resistor array (FSR) is used to capture tactile (haptic) imprints. Trained classifiers are finally tested to recognize data from new objects of same categories.