With the goal of providing an alternative to optical and other tactile sensors, we set out to stress test the object shape identification capabilities of barometric tactile arrays in robotic manipulation tasks. These sensors are superior to optical devices in terms of form factor, ease of fabrication, and data reading/processing speeds, but lack the necessary spatial resolution to identify surface shapes via a single contact. To compensate, we utilize in-hand-manipulation, specifically in- hand-rolling to identify object shapes via a spatiotemporal approach. To increase task difficulty, we only use three neighboring barometric sensors and designed strict experiment requirements with the purpose of creating a set of extremely confusable test objects. The E- TRoll robotic hand, equipped with a barometric tactile array on one finger, was used to roll test objects within its grasp, taking just under 3.4 seconds for data collection under the fastest tested speed setting, compared to 33 seconds in our previous work. We also designed and implemented a feature extraction algorithm, based and improved upon our recently published algorithm. This captures enough information from the collected spatiotemporal data samples for successful classification with only 13 features. Finally, a bagged tree classification algorithm was trained and optimized with data from 1,164 trials of rolling 9 prismatic test objects, leading to a five-fold cross validation accuracy of 90.5% for identifying the 9 object classes.