In this paper, the wavelet transform is used on surface mount device (SMD) images to devise a system used to inspect the presence of SMDs in printed circuit boards. The complete system involves preprocessing, feature extraction, and classification. The images correspond to three cases: SMD present (SMD), SMD not present with a speck of glue (GLUE), and SMD not present (noSMD). For each case, two images are collected using top and side illuminations but these are first combined into one image before proceeding to do further processing. Preprocessing is done by applying the wavelet transform to the images to expose details. Using 500 images for each of the three cases, various features are considered from different wavelet subbands, using one or several transform levels, to find four good discriminating parameters. Classification is performed sequentially using a two-level binary decision-tree. Two features are combined into a two-component feature vector and are fed into the first level that compares the SMD vs noSMD cases. The second level uses another feature vector produced by combining two other features and then compares the SMD and GLUE cases. The features used give no cluster overlap on the training set and simple parallelpiped classifier is devised at each level of the tree producing no errors on this set. Results give 99.6% correct classification when applied to a separate testing set consisting of 500 images for each case. All the errors are made to level 2 classifying six SMD images erroneously as GLUE.