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This paper presents hidden Markov models (HMM) approach for forecasting stock price for interrelated markets. We apply HMM to forecast some of the airlines stock. HMMs have been extensively used for pattern recognition and classification problems because of its proven suitability for modelling dynamic systems. However, using HMM for predicting future events is not straightforward. Here we use only one HMM that is trained on the past dataset of the chosen airlines. The trained HMM is used to search for the variable of interest behavioural data pattern from the past dataset. By interpolating the neighbouring values of these datasets forecasts are prepared. The results obtained using HMM are encouraging and HMM offers a new paradigm for stock market forecasting, an area that has been of much research interest lately.