Thanks a lot for the reply. Yes, in my case any mis-classification is equally inaccurate. However, in case of binomial output, we use the raw pronsities (probability of positive outcome) to consutruct the ROC curve and hence calculate the AUC. In a multinomial case as this one, I do not have raw propensities. So the best thing that I can think of is using the rate of misclassifications to assess a models accuracy. But if anyone knows a stronger method (like AUCs for binomial), please do let me know.