This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant of the hidden Markov model in which all the hidden state transitions do not necessarily emit observable events. This property of POM is used to model, as the hidden process, a common search behavior that users would read and skip search results, leaving no observable user actions to record in the search logs. The Markov nature of the model further lends support to cope with the facts that a single observed sequence can be probabilistically associated with many hidden sequences that have variable lengths, and the search results can be read in various temporal orders that are not necessarily reflected in the observed sequence of user actions. To tackle the implementation challenges accompanying the flexibility and ana-lytic powers of POM, we introduce segmental Viterbi algorithm based on segmental decoding and Viterbi training to train the POM model parameters and apply them to uncover hidden processes from the search logs. To validate the model, the latent variables modeling the browsing patterns on the search result page are compared with the experimental data of the eye tracking stu-dies. The close agreements suggest that the search logs do contain rich information of user behaviors in browsing the search result page even though they are not directly observable, and that using POM to understand these sophisticated search behaviors is a promising approach.