Object recognition technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, although the image of the objects may vary somewhat in different viewpoints, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades In many computer vision systems, object detection is the first task being performed as it allows us to obtain further information regarding the detected object and about the scene. In recent years, the use of deep learning has attracted the attention of researchers, Deep learning uses multiple layers to extract raw features from high-level features the machine divides each complex concept into simpler concepts. The proposed method is based on AlexNet architecture. In this method, a convolutional neural network-based architecture with a small amount of data can detect objects in the image, the data used is the March database, so this process identifies thirty pictures in four separate classes with 100% accuracy.