Single-molecule methods were developed with the objective to obtain kinetic information of biomolecular processes from a single biomolecule. This objective can be achieved by analyzing the binding events in recordings (“trajectories”) of single molecules (e.g., using fluorescence); the so-called dwell times. This study used two automated methods to analyze experimental single-molecule data. First, a method called AutoStepfinder is an unsupervised classification and idealization of single-molecule trajectories to find kinetic states. The method iteratively fits steps at locations that yield the biggest reduction in the variance. Second, the variational Bayesian inference method, coupled with a hidden Markov model, vbFRET, was also used. This method applied variational Bayesian approach to estimate the posterior parameters and numbers of hidden state values. We apply those two methods to analyze the single molecule ATPase experiments obtained by total internal reflection fluorescence (TIRF) microscopy on immobilized myosin motor enzymes attached to a coverslip surface. The dwell time analysis results obtained using the AutoStepfinder and vbFRET methods were compared to manual analysis in order to elucidate how these methods can facilitate automated analysis of single-molecule binding experiments.