Given a sequence (T 1 , T 2 , . . . ) of random d × d matrices with nonnegative entries, suppose there is a random vector X with nonnegative entries, such that i≥1 T i X i has the same law as X, where (X 1 , X 2 , . . . ) are i.i.d. copies of X, independent of (T 1 , T 2 , . . . ). Then (the law of) X is called a fixed point of the multivariate smoothing transform. Similar to the well-studied one-dimensional case d = 1, a function m is introduced, such that the existence of α ∈ (0, 1] with m(α) = 1 and m ′ (α) ≤ 0 guarantees the existence of nontrivial fixed points. We prove the uniqueness of fixed points in the critical case m ′ (α) = 0 and describe their tail behavior. This complements recent results for the non-critical multivariate case. Moreover, we introduce the multivariate analogue of the derivative martingale and prove its convergence to a non-trivial limit.(1.2) limThe function L is constant if m ′ (α) < 0 and L(t) = (|log t| ∨ 1) if m ′ (α) = 0, the latter being called the critical case. For α < 1, the property (1.2) implies that the fixed points have Pareto-like tails with index α, i.e. lim t→∞ t −α P (X > t) /L(1/t) ∈ (0, ∞), see [26] for details. Tail behavior in the case α = 1, in which there is no such implication, is investigated in [21,26,16].