In this paper

for a nuclear information system driven by a 252Cf spontaneous fission neutron source

the random nuclear signals were analyzed and processed using both Hilbert-Huang Transform(HHT) and artificial neural networks(ANN).We got its time-frequency features and recognized its nuclear signal samples based on their classification.The results show that the time-frequency feature extraction method based on Hilbert marginal spectrum is applicable to separate the time-frequency feature of non-stationary nuclear random signal because it can reflect the time-frequency distributions.After classified recognition of nuclear signal samples using BP neural network

we also got ideal result that further verified the effectiveness and reasonability of the method.