Classification of marine targets using radar data products has become an important area for modern research society. However, due to several reasons such as the similarity between ship structures and spatial specifications, classification of marine targets constitutes a challenging problem. In almost all of the studies, this problem has been handled by focusing on a single instance of range profiles or synthetic aperture radar data. However, this approach is seen to achieve only a particular success. This study introduces a novel classification approach that is shown to provide additional classification enhancements by exploiting the extra information extracted from sequential range profiles generated by ground-based marine surveillance radars. With this purpose, both synthetic and measuremental range profiles are taken into consideration. Synthetic profile data are generated for seven marine targets by using an electromagnetic scattering simulation tool (RASES)1. On the other hand, a total of 2387 range profile data of 171 different target tracks are collected for five different marine target class types by using an X-band marine surveillance radar. Each target tracked for a long period of time to gather sequential HRRP data subsets. HRRP data subsets are used to generate HMM based transition matrix probabilities and sequential classification results by evaluating proposed method. Probabilistic neural network (PNN) and convolutional neural network (CNN) classification algorithms applied to gather classification results. The proposed method results are compared with both single value classification and majority voting rule (MVR) method results. According to the examination results, the proposed classification approach provides remarkable enhancements in the correct classification rates when compared to the case of single profile data approach.