We developed a software named LiverANN based on artificial neural network (ANN) technique for distinguishing the pathologies of focal liver lesions in magnetic resonance (MR) imaging, which helps radiologists integrate the imaging findings with different pulse sequences and raise the diagnostic accuracy even with radiologists inexperienced in liver MR imaging. In each patient, regions of focal liver lesion on T1-weighted, T2-weighted, and gadolinium-enhanced dynamic MR images obtained in the hepatic arterial and equilibrium phases were placed by a radiologist (M.K.), then the program automatically calculated the brightness and homogeneity into numerical data within the selected areas as the input signals to the ANN. The outputs from the ANN were the 5 categories of focal hepatic diseases: liver cyst, cavernous hemangioma, dysplasia, hepatocellular carcinoma, and metastasis. Fifty cases were used for training the ANN, while 30 cases for testing the performance. The result showed that the LiverANN classified 5 types of focal liver lesions with sensitivity of 93%, which demonstrated the ability of ANN to fuse the complex relationships among the image findings with different sequences, and the ANN-based software may provide radiologists with referential opinion during the radiologic diagnostic procedure.