Robots need to be able understand the haptic sensations that occur during physical interaction in order to recognize and manipulate objects. This paper centers on the problem of robotic toolmediated haptic surface recognition. We collected data as a PR2 robot interacted with fifteen different surfaces through a novel tool that captures high-definition recordings of tactile vibrations, contact forces, and tool position. We then developed algorithms for using the recorded data to recognize the identity of the surface under varying contact conditions. We show that successful recognition of surfaces touched through a tool critically depends on accounting for the physical contact information (tool speed and normal force). Additionally, we present several enhancements to improve surface recognition including the combination of multiple vibration axes into a single axis, using frictional force during contact, and logarithmically spaced frequency scaling for vibration analysis. We implement a flexible classifier composed of multiple One-Class Support VectorMachines trained on a data set containing all fifteen surfaces, and we then use this classifier to identify these same surfaces in a new data set recorded under substantially different contact conditions, achieving an overall texture recognition rate of 80%.