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Abstract. This paper studies the partial information control problems of backward stochastic systems. There are three major contributions made in this paper: (i) First, we obtain a new stochastic maximum principle for partial information control problems. Our method relies on a direct calculation of the derivative of the cost functional. (ii) Second, we introduce two classes of partial information linear-quadratic backward control problems for the first time and then investigate them using the maximum principle. Complete and explicit solutions are obtained in terms of some forward and backward stochastic differential filtering equations. (iii) Last but not least, we study a class of full information stochastic pension fund optimization problems which can be viewed as a special case of our general partial information ones. Applying the aforementioned maximum principle, we derive the optimal contribution policy in closed-form and present some related economic remarks. ). In particular, the celebrated Black-Scholes option pricing formula can be derived from a class of linear BSDEs, where the random terminal condition is just the option's payoff at the maturity.Since BSDEs are well-defined dynamic systems, it is very natural and appealing to consider the control problems of BSDEs. However, there exist only a few works along this line, including Peng [13], Xu [22], Wu [19], Lim and Zhou [7], and Wang and Yu [17]. Our work distinguishes itself from the above ones in the following aspects: (i) Our work is established in the context of partial information which is rather general than that of the partial observation. In fact, our information can be summarized by any subfiltration and free of specific observation structures; thus it includes the partial observation models (in particular, the white noise observation models) as its special cases (see, e.g., Wu [20], Wang and Wu [15]). (ii) Two important classes of partial