This paper presents a optic system and a visual inspection algorithm looking for solder joint defects of J-lead chip which are more integrate and smaller than ones with Gull-wing on PCBs(Printed Circuit Boards). The visual inspection system is composed of three sections : host PC, imaging and driving parts. The host PC part controls the inspection devices and executes the inspection algorithm. The imaging part acquires and processes image data. And the driving part controls XY-table for automatic inspection. In this paper, the most important five features are extracted from input images to categorize four classes of solder joint defects in the case of J-lead chip and utilized to a back-propagation network for classification. Consequently, good accuracy of classification performance and effectiveness of chosen five features are examined by experiment using proposed inspection algorithm.