Ultrasound imaging is one of the primary modalities used for diagnosing a multitude of medical conditions affecting organs and soft tissues the body. Unlike X-rays, which use ionizing radiation, ultrasound imaging utilizes non-hazardous acoustic waves and is widely preferred by doctors. However, ultrasound imaging sometimes requires substantial manual effort in the identification of organs during real-time scanning. Also, it is a challenging task if the scanning performed by an unskilled clinician does not comprise adequate information about the organ, leading to an incorrect diagnosis and thereby fatal consequences. Hence, the automated organ classification in such scenarios can offer potential benefits. In this paper, We propose a convolutional neural network-based architecture (CNNs), precisely, a transfer learning approach using ResNet, VGG, GoogleNet, and Inception models for accurate classification of abdominal organs namely kidney, liver, pancreas, spleen, and urinary bladder. The performance of the proposed framework is analyzed using in-house developed dataset comprising of 1906 ultrasound images. Performance analysis shows that the proposed framework achieves a classification accuracy and F1 score of 98.77% and 98.55%, respectively, on an average. Also, we provide the performance of the proposed architecture in comparison with the state-of-the-art studies. © 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences