The availability of very high resolution (VHR) satellite imagery (<1m) has opened new vistas in large scale mapping and information management in urban environments. Buildings are the most essential dynamic incremental factor in urban environment and hence their extraction is the most challenging activity. Extracting the urban features particularly buildings using traditional pixel based classification approaches as a function of spectral tonal value produces relatively less accurate results for these VHR Imageries. The present study demonstrates the building extraction using Pleiades panchromatic (PAN) and multispectral stereo satellite datasets of highly planned and dense urban areas in parts of Chandigarh, India. The stereo datasets were processed in photogrammetric environment to obtain the Digital elevation Model (DEM) and corresponding orthoimages. DEM’s were generated at 0.5 m and 2.0 m from stereo PAN and multispectral datasets, respectively. The orthoimages thus generated were segmented using Object Based Image Analysis (OBIA) tools. The object primitives such as scale parameter, shape, textural parameters, DEM derivatives were used for segmentation and indices were used to determine threshold values for building fuzzy rules for building extraction and classification. The rule based classification was carried out with defined decision rules based on object primitives and fuzzy rules. Two different methods were utilized for performance evaluation of the proposed automatic building approach. Overall accuracy, correctness and completeness were evaluated for extracted buildings. It was observed that overall accuracy was higher (> 93%) in areas having larger buildings and sparse built-up as compared to areas having smaller buildings and dense built-up.