In this paper we present how Bag-of-Features Hidden Markov Models can be applied to printed Bangla word spotting. These statistical models allow for an easy adaption to different problem domains. This is possible due to the integration of automatically estimated visual appearance features and Hidden Markov Models for spatial sequential modeling. In our evaluation we are able to report high retrieval scores on a new printed Bangla dataset. Furthermore, we outperform state-of-the-art results on the well-known George Washington word spotting benchmark. Both results have been achieved using an almost identical parametric method configuration.