This paper presents recent ATR results with High Range Resolution (HRR) profiles used for classification of ground targets. Our previous work has demonstrated that effective HRR-ATR performance can be achieved if the templates are formed via Singular Value Decomposition (SVD) of detected HRR profiles and the classification is performed using normalized Matched Filtering (MF) [1, 2]. It had been shown theoretically in [1, 2] that the eigen-vectors are the optimal feature set representation of a collection of HRR profile vectors and we had proposed to use the dominant range- space eigen-vectors as templates, known as Eigen-Templates (ET). However, in [1, 2], HRR-ATR performance using the Eigen Template-Matched Filter (ETMF) combination had been applied to the forced decision case only using the XPATCH data sets. In this paper, we demonstrate the effectiveness in HRR- ATR performance of the ETMF approach by incorporating unknown target scenario . All results in this paper use the public release MSTAR data. Furthermore, in our earlier work, HRR testing data was used without any additive noise, where it was found that detected-HRR data preprocessed by Power Transform (PT) can enhance ATR performance. However, results and analysis presented in this paper demonstrate that PT pre- processing when applied to noisy observation profiles tend to obscure the target information in the HRR profiles considerably which in turn leads to considerable deterioration in HRR-ATR performance. Hence, we argue in this paper that PT pre-processing should be avoided in practice in all HRR-ATR implementations. Instead, we show that the proposed ETMF with appropriate alignment and normalization of template and observation profiles can achieve excellent HRR-ATR performance.Extensive simulation studies have been carried out to validate the proposed approach. Results are presented for different noise levels in terms of Receiver Operating Characteristics (ROC) curves.