The western Himalayan state of Himachal Pradesh is known as the hydro-power state of India with associated social and environmental problems of large hydro power plants. The reduced water inflow in the rivers during extreme winters affects power generation in the state. Therefore solar and wind resources need to be utilized to supplement power generation requirements. With this objective the prospects of photovoltaic-micro wind based hybrid systems are studied for 12 locations of the state. The NASA data, Artificial Neural Network predicted and ground measured data are used in the analysis of Hamirpur location whereas for remaining 11 locations estimated, NASA and Artificial Neural Network predicted data are used, as measured solar and wind data are not available for most of the locations in the state. Root Mean Square Error between three input data types are found to range from 0.08 to 1.89. The results show that ANN predicted data are close to measured/estimated data. A 6 kWp roof mounted photovoltaic-micro wind hybrid system at Hamirpur with daily average energy demand of 5.2 kWh/day is studied. This system specifications are used to obtain optimum PV-micro wind based hybrid power system configurations for all locations. The optimum configuration for Hamirpur is found to be a 5 kWp micro wind turbine, 2 kW converter, 10 batteries and 8 kWp PV system whereas for other 11 locations a 5 kWp micro wind turbine, 2 kW converter, 10 batteries and 2-9 kWp PV systems are obtained. The normalized solar and wind energy generation are found to range between 1034-1796 kWh/kWp/yr and 222-616.8 kWh/kWp/yr respectively for all locations. The study shows that state has good prospect of power generation from hybrid systems with major solar and minor wind components. However, a detailed follow up wind resource assessment programme is needed for the Himalayan region to identify true wind penetration for wind based solar hybrid power systems. The methodology presented can be used for the prediction of the photovoltaic and wind power generation potential for any region worldwide. (C) 2015 Elsevier Ltd. All rights reserved.