The quality of prebaked carbon anodes, consumed in electrolysis during the primary aluminum production, has an important impact on the cell performance. The anode quality depends on the raw material quality and operating conditions in the anode plant. Development of simple, quick, and inexpensive techniques and tools for anode quality control will help industry identify the source of problems and take the necessary corrective actions rapidly. In this article, different quality control tools developed to find optimum vibration time, pitch content in green anode, metallic impurity content, wettability of coke by pitch, effect of mixing on coke particle size distribution, and measurement of green and baked anode electrical resistivities will be presented. In parallel, data analysis using the artificial neural network (ANN), a powerful statistical tool for such applications, provides complementary information on quality and process. This article will present also the potential utilization of ANN in quality control.