In reply to Joe.

That's an interesting question. I assume that you mean a weight for each observation. I believe the answer is yes. You can take your data matrix, X, and multiply each row by the square root of the weight for that row. If you call the new matrix Y, then Y=sqrt(w)#X. The classical covariance matrix for Y is therefore proportional to X`*W*X, which is the classical weighted scatter matrix. The MCD matrix will be a robust weighted covariance estimate. I haven't actually done it, but this method seems valid.