A various techniques have been presented in this paper are based upon the structure or geometrical shape of an object and by extracting these features, we can detect an object and recognize the same. In this work, we are firstly detecting or counting a number of objects available in an image, then each object is cropped and resized and boundary values of an object is detected, which helps in extracting the relevant features of an object. A number of features which are extracted in this work are contiguous horizontal and vertical peak extent feature, non-connected and connected contour segment features, vertical and horizontal balanced division point, and chord features etc. All of these features further help in finding the shape of an object and it will further help in detection and recognition of an object. In this work, we use linear-SVM and k-NN classifiers for classifying of an object. In this work, we have taken total 1020 images from mpeg dataset, these images includes both i.e. training and testing. The dataset includes total 51 classes and each class contains 20 images. In this, we achieve the accuracy of 91.0% and 90.0% by using Linear-SVM classifier for object recognition using proposed vertical and horizontal peak extent feature extraction technique.