We a controlled system driven by a coupled forward-backward stochastic differential equation (FBSDE) with a non degenerate diffusion matrix. The cost functional is defined by the solution of the controlled backward stochastic differential equation (BSDE), at the initial time. Our goal is to find an optimal control which minimizes the cost functional. The method consists to construct a sequence of approximating controlled systems for which we show the existence of a sequence of feedback optimal controls. By passing to the limit, we establish the existence of a relaxed optimal control to the initial problem.The existence of a strict control follows from the Filippov convexity condition. Our result improve in some sense those of [4,6].problem. This allows them to construct a sequence of optimal feedback controls. Then, they pass to the limit and use the result of [10] in order to get the existence of a relaxed optimal control. In both papers [4] and [6] the Filippov convexity condition is used in order to get the existence of a strict optimal control. It should be noted that in [4] and [6] the controlled system is driven by a decoupled FBSDE.The aim of the present paper is to extend the results of [4,6], to a coupled FBSDE. To begin, let us give a description of our problem.Let T > 0 be a finite horizon, t ∈ [0, T ] and (Ω, F, P, (F t )) be a filtered probability space satisfying the usual conditions. Let W be a d-dimensional Brownian motion with respect to the filtration (F t ). Let U be a compact metric space. We define the deterministicWe consider the following controlled coupled FBDSE defined for s ∈ [t, T ] by:dX t,x,u s = b(X t,x,u s , Y t,x,u s , u s )ds + σ(X t,x,u s , Y t,x,u s )dW s , X t,x,u t = x, dY t,x,u s = −f (X t,x,u s , Y t,x,u s , Z t,x,u s , u s )ds + Z t,x,u s dW s + dM t,x,u s , M t,x,u tSince V δ ′ is a classical solution to the Hamilton-Jacobi-Bellman equation it follows thatHence, the comparison Theorem shows thatUsing a symmetric argument, we deduce that :Since V δ and V δ ′ are deterministic, we haveHence, it suffices to estimate E(|Y δ ′ ,t ′ ,x ′ ,u δ t ′ − Y δ,t,x,u δ t |).We assume that t ′ < t, and for