Summary: NoRSE was developed to analyze high-frequency datasets collected from multistate, dynamic expts., such as mol. adsorption and desorption onto carbon nanotubes. As technol. improves sampling frequency, these stochastic datasets become increasingly large with faster dynamic events. More efficient algorithms are needed to accurately locate the unique states in each time trace. NoRSE adapts and optimizes a previously published noise redn. algorithm and uses a custom peak flagging routine to rapidly identify unique event states. The algorithm is explained using exptl. data from our lab and its fitting accuracy and efficiency are then shown with a generalized model of stochastic datasets. The algorithm is compared to another recently published state finding algorithm and is found to be 27 times faster and more accurate over 55% of the generalized exptl. space. NoRSE is written as an M-file for Matlab. [on SciFinder(R)]