Commercial supply, from several steel manufacturers, of low-silicon non-grain-oriented electrical steel was monitored over a span of several years. A total of 51 samples were selected-selected from many hundreds on the basis of large differences in magnetic properties, but absence of significant variations in chemistry (other than differences in silicon percentage). The selected samples were analyzed for crystallographic texture and for grain size. The data were carefully analyzed to bring out the effects of metallurgical variables, namely silicon %, grain size and crystallographic texture, on the magnetic properties using explicit functional relationships as well as artificial neural network (ANN). Among the explicit relationships, power law relationship appears to offer a best fit between magnetic properties and the metallurgical factors. ANN approach to the relationship, however, brought out predicted values with least error