This paper describes the problems associated with the implementation of Bayesian target attribute fusion algorithms when the track identification (ID) information is received at the fusion center over several types of communication links, and nothing is known about the types of sensors used to derive such information. A typical message format that is frequently used consists of the following: friend, foe, or neutral, etc.; fighter, bomber, etc.; and platform specific target types (F-15, MIG-29, etc.). It is assumed that the track information received over the link is a result of multisensor ID fusion at the transmitting stations that employ soft decision fusion and provide confidence level or probabilities associated with the message. At the command center, these off-board target IDs must be fused in order to resolve conflicts among originating message sources and to increase confidence in the fused ID. If the message format contains N attributes, Bayesian approach to M-ary decision theory requires 2N definitions of type I and type II error probabilities and their respective priors for a probabilistically exact procedure, which could be computationally prohibitive. This problem can be circumvented by mapping the N-dimensional decision space into one of lower dimension and manipulating the thresholds involved in the likelihood ratio test in reduced dimension. Such an approach ignores the requirement that the hypothesis space be mutually exclusive and exhaustive; otherwise strong inconsistency in decision results. In this paper, the errors involved in making these two assumptions are discussed and the effect of dependence of evidence obtained at each source is also explored.