This short paper describes the distributed recognition of targets. The sensor data fusion of features arising from multiple sensors is considered for the purpose of target recognition/classification. This is performed in a scenario wherein the underlying distributions are not Gaussian (i.e., the distributions do not obey Normality). Furthermore, there is 'correlation' between the separate sensor features. The separate (sensor) features are not statistically independent. The data fusion procedure pursued here does not find itself in the object identification sensor data fusion paradigm. It is an intermediate step between the two levels of data fusion for target recognition. In a departure from the (class) conditional assumptions typically made, another factorization of the joint conditional distribution is evaluated. This factorization requires the conditioning on previous feature vectors. A novel adaptive procedure is suggested to address that alternate factorization. A non-standard nonparametric classification procedure is detailed in providing the classification results. The classification/recognition results are for multiple classes. Results are compared against the centralized method and the statistically independent method.