This correspondence presents two robust solutions to the control of the output probability density function for general multiinput and multioutput stochastic systems. The control inputs of the system appear as a set of variables in the probability density functions of the system output, and the signal available to the controller is the measured probability density function of the system output. A new type of dynamic probability density model is formulated by using a B-spline neural network with all its weights dynamically related to the control input. It has been shown that the so-formed robust control algorithms can control the shape of the output probability density function and can guaranteed the closed-loop stability when the system is subjected to a bounded unknown input, An illustrative example is included to demonstrate the use of the developed control algorithms, and desired results have been obtained.