Assortative matching is a network phenomenon that arises when nodes exhibit a bias towards connections to others of similar characteristics. While mixing patterns in networks have been studied in the literature, and there are well-defined metrics that capture the degree of assortativity (e.g., assortativity coefficient), the latter deal only with single-dimensional enumerative or scalar features. Nevertheless, various complex behaviors of network entities—e.g., human behaviors in social networks—are captured through vector attributes. To date, no formal metric able to cope with similar situations has been defined. In this paper, we propose a novel, two-step process that extends the applicability of the assortativity coefficient to multi-dimensional attributes. In brief, we first apply clustering of the vertices on their vector characteristic. After clustering is completed, each network node is assigned a cluster label, which is an enumerative characteristic and we can compute the assortativity coefficient on the cluster labels. We further compare this method with an alternative baseline, which is an immediate extension of the assortativity coefficient, namely, the assortativity vector. The latter treats each element of the node’s attribute vector separately and then combines the independent results in a single value. Finally, we apply our method and the baseline on two different social network datasets. We also use synthetic network data to delve into the details of each metric/method. Our findings indicate that while the baseline of assortativity vector performs satisfactory when the variance of the elements of the vector attribute across the network population is kept low, it provides biased results as this variance increases. On the contrary, our approach appears to be robust in such scenarios.