Involving a limited resource, the assessment of groundwater aquifers is of utmost importance. A key component of any such assessment is the determination of key properties that permit water resource managers to estimate aquifer drawdown and safe yield. This paper presents a particle filtering approach to estimate aquifer properties from transient data sets, leveraging recently published analytically-derived models for confined aquifers and using sample-based approximations of underlying probability distributions. The approach is examined experimentally through validation against three common aquifer testing problems: determination of (i) transmissivity and storage coefficient from non-leaky confined aquifer performance tests, (ii) transmissivity, storage coefficient, and vertical hydraulic conductivity from leaky confined aquifer performance tests, and (iii) transmissivity and storage coefficient from non-leaky confined aquifer performance tests with noisy data and boundary effects. On the first two well-addressed problems, the results using the particle filter approach compare favorably to those obtained by other published methods. The results to the third problem, which the particle filter approach can tackle more naturally than the previously-published methods, underscore the flexibility of particle filtering and, in turn, the promise such methods offer for a myriad of other geoscience problems.