In this paper, evaluation is made on the result of CBIR system based on different types of wavelets from wavelet family like haar, daubechies, coiflet and symlet wavelet transform with Euclidean distance for similarity matching to calculate the deviation from query image. In this paper discrete wavelet transform is used to decompose the image and two different strategies are used for feature extraction. The image is decomposed till sixth level using different wavelets. In this paper two different experiments are carried out for two different methods evaluation. In first experiment the last level approximate component of decomposed image considered as a feature vector while in second experiment all the level approximate component is taken into consideration. All the approximate component is concatenated and used as a feature vector. Experiments results are tabulated. COIL image database is considered. The experiment is performed on different set of images, 720 having 10 different classes, 1440 having 20 different classes and 2160 images having 30 different classes for comparisons purpose. The result is tabulated at the end. Keywords— CBIR, QBIC, Precision, Recall, Query Image, Distances, Efficiency, HAAR, Chessboard Distance, City Block Distance, Euclidean Distance.Introduction.