Help with multidimensional scaling for multiple DVs

Hello statistics experts!

I couldn't find an answer to my question so I thought that one of you might know:
I have 14 risk sources, like nuclear power plants (let's call them "objects"), and participants judged them on 14 characteristics (e.g., potential danger). Now, I would like to create a classification of the objects based on the data (to understand which are "closer" in terms of the way people perceive them). If there was just one characteristic, I could use traditional Multi-Dimensional Scaling (MDS), for example. This was done in that past by averaging the correlation across characteristics (which is really an inappropriate solution these days). Is there a way of conducting MDS for vectors? (so that each object will be defined as a vector of his characteristics). Any other idea for an analysis that will allow for clusters to emerge will also be welcomed.

Thank you very much for your help!