The problem is to recover stochastic processes from an unknown stationary linear transform. Our contribution is two-fold. First we focus on instantaneous mixtures: observation e(t) is assumed to write as a regular linear transform of the sources, x(t), as e(t)=B_0x(t). The only assumption requested is that the sources x_i(t) are mutually independent, and no additional knowledge upon their statistics is necessary provided they are not normal. Extensions to convolutional mixing are then pointed out, namely cases where e(t)=A(t)*x(t) where A(t) has a rational transfer function. Sensitive improvements to the algorithm of Giannakis et al for MA identification are included. Multivariate ARMA identification can be split intothree successive estimation problems: AR identification, monic MA identification, and estimation of B_0 in last position.