In the modern knowledge economy, Higher Education Institutions are being required to operate more entrepreneurially, commercializing the results of their research and spinning out new, knowledge-based enterprises. However, the possibility to engage in entrepreneurial behaviours varies substantially between regions and countries. As a post-communist country, Romania faces numerous constraints in this respect. According to Erawatch Country Report (2010), technology transfer activities from universities to business firms are relatively limited, due to a low demand from industry and also relatively weak offer from universities, but many universities are currently actively involved in strengthening their technology transfer capacity. This paper explores different patterns of academic knowledge commercialization in 90 Romanian universities, using the data collected by the Romanian Ministry of Education, Research, Youth and Sports in 2011. In this purpose, we have used the discriminant analysis, due to its advantages in both synthesizing a set of variables and expressing the relationships between them. The discriminant variable by which we divided the universities in groups was the commercial (licensing) income generated by the 90 Romanian universities in 2010. The statistical observation has been carried out on a set of eight variables that were previously standardised using the Z-score technique and tested for normal distributions and homogeneity of variances. The test F for Wilks's Lambda was significant at 0.05 for four of our variables (FTE research staff, research expenditure, patent applications at EPO and new products) and had a Sig. smaller or equal to 0.1 for another four variables (patent applications in Romania, R&D grants with domestic private funding, number of partnerships with private companies and sponsorships). The first discriminate function accounting for 55,8 of between group variability revealed four significant predictors, of which research expenditure (,811*) was by far the strongest one. The other four predictors were grouped under the second canonical discriminate function. The cross validated classification showed that 58,9% of original grouped cases were correctly classified. Finally, we have grouped the universities by their region of origin and placed them in a 2x2 matrix that reflects their position in relation to the two discriminant functions. Policy implications aimed at improving academic knowledge commercialization at each region level are further advanced.