  This paper discusses several techniques which may be used for applying the
coupling method to solutions of stochastic differential equations (SDEs). They
all work in dimension $d\ge 1$, although, in $d=1$ the most natural way is to
use intersections of trajectories, which requires nothing but strong Markov
property and non-degeneracy of the diffusion coefficient. In dimensions $d>1$
it is possible to use embedded Markov chains either by considering discrete
times $n=0,1,\ldots$, or by arranging special stopping time sequences and to
use local Markov -- Dobrushin's (MD) condition. Further applications may be
based on one or another version of the MD condition. For studies of convergence
and mixing rates the (Markov) process must be strong Markov and recurrent;
however, recurrence is a separate issue which is not discussed in this paper.
