The application of object recognition technology has been increased with a growing need to introduce automated system in industry. However, object transformed by noises and shadows appeared from illumination causes challenge problem in object detection and recognition. In this paper, an illumination invariant object detection using a DoG filter and adaptive threshold is proposed that reduces noises and shadows effects and reserves geometry features of object. And also, rotation invariant object recognition is proposed that has trained with neural network using classes categorized by object type and rotation angle. The simulation has been processed to evaluate feasibility of the proposed method that shows the accuracy of 99.86% and the matching speed of 0.03 seconds on ETRI database, which has 16,848 object images that has obtained in various lighting environment.