The　homodyned　K　(HK)　distribution　allows　a　general　description　of　ultrasound　backscatter　envelope　statistics　with　specific　physical　meanings.　In　this　study,　we　proposed　a　new　artificial　neural　network　(ANN)　based　parameter　estimation　method　of　the　HK　distribution.　The　proposed　ANN　estimator　took　advantages　of　ANNs　in　learning　and　function　approximation　and　inherited　the　strengths　of　conventional　estimators　through　extracting　five　feature　parameters　from　backscatter　envelope　signals　as　the　input　of　the　ANN:　the　signal-to-noise　ratio　(SNR),　skewness,　kurtosis,　as　well　as　Xand　U-statistics.　Computer　simulations　and　clinical　data　of　hepatic　steatosis　were　used　for　validations　of　the　proposed　ANN　estimator.　The　ANN　estimator　was　compared　with　the　RSK　(the　level-curve　method　that　uses　SNR,　skewness,　and　kurtosis　based　on　the　fractional　moments　of　the　envelope)　and　XU　(the　estimation　method　based　on　Xand　U-statistics)　estimators.　Computer　simulation　results　showed　that　the　relative　bias　was　best　for　the　XU　estimator,　whilst　the　normalized　standard　deviation　was　overall　best　for　the　ANN　estimator.　The　ANN　estimator　was　almost　one　order　of　magnitude　faster　than　the　RSK　and　XU　estimators.　The　ANN　estimator　also　yielded　comparable　diagnostic　performance　to　state-of-the-art　HK　estimators　in　the　assessment　of　hepatic　steatosis.　The　proposed　ANN　estimator　has　great　potential　in　ultrasound　tissue　characterization　based　on　the　HK　distribution.