A recent study on symbolic data analysis literature reveals that symbolic distance measures are playing a major role in solving the pattern recognition and analysis problems. After a careful study on the existing symbolic distance measures, we have identified that most of the existing symbolic distance measures either suffer from generalization or do not address object variability. To alleviate these problems we are proposing new generalized Similarity symbolic distance measure. The proposed distance measure is asymmetric, addresses object variability, and obeys partial order. To leverage the advantages of both fuzzy set theory and symbolic data analysis, conventional classification algorithm that works on the principles of fuzzy equivalence relation has been extended to handle Symbolic data. Efficacies of the proposed techniques are validated by conducting several experiments on the well-known assertion type of symbolic data sets with known classification results.