Automatic Instrument recognition in sound recordings is a traditional method and has been gaining a lot of attention since the last decades due to the advent of music streaming services like Spotify, Apple Music, Deezer etc. Distinction between similar instruments like Cello and Violin, Flute and Clarinet still remains a challenging task for machines and even humans. This research paper is an effort to identify 4 similar String instruments (Acoustic Guitar, Cello, Violin and Electric Guitar) in the audio recordings. We have used 1D MLPs and 2D CNN architectures for classifying the sounds and compared the performance on different audio features. Our experiments also show that using image based transfer learning models like Inception and VGG gives better results among the aforementioned architectures.