The advancement in computer vision field with the help of deep learning methods is significant. The increase in computational resources, have lead researchers developing solutions that could help them in achieving high accuracy in image segmentation tasks. We performed segmentation of different types of objects in the chromatography instruments used in GE Healthcare, Uppsala. In this thesis project, we investigated methods in Computer vision and deep learning to segment out the different type of objects in instrument image. For a machine to automatically learn the features directly from instrument image, a deep convolutional neural network was implemented based on a recently developed existing architecture. The dataset was collected and preprocessed before using it with the neural network model. The model was trained with two different architecture Unet and Segnet developed for image segmentation. Both the used architecture is efficient and suitable for semantic segmentation tasks. Among different components to segment out in the instrument, there was a thin pipe. Unet was able to achieve good results while segmenting thin pipes with fewer data as well. Results show that Unet can act as a suitable architecture for segmenting different objects in an instrument even if we have only 100 images. Further advances can be done to improve the performance of the model by generating a better mask of the model and finding a way to collect more data for training the model.