This talk addresses the development of imaging techniques for the early detection of breast cancer, based on Ultra Wideband (UWB) radar, a promising emerging technology that exploits the dielectric contrast between normal and tumour tissues at microwave frequencies. Of particular interest in this work are issues related to techniques for classification of potential breast tumours into benign and malignant. This is particularly important given the results from recent studies of the dielectric properties of breast and tumour tissue, which have found that strong similarities exist between the dielectric properties of malignant, benign and normal fibroglandular breast tissue. This creates a more challenging imaging scenario and motivates the development of enhanced signal processing techniques for UWB imaging systems. Tumour growth and development patterns are modelled using Gaus- sian Random Spheres, using four discrete sizes and four different shapes. Feature extraction methods including Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT), are used to extract the most relevant features from the detailed Radar Target Signatures of the tumours, which are then classified with a number of different classification techniques: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM). In addition to these techniques, a number of different multi-stage classification architectures are consid- ered. The feature extraction and classification algorithms are evaluated for both homogeneous and heterogeneous breast tissue models, for a range of different tumour sizes and shapes. Also, the first experimental results using a pre-clinical UWB prototype imaging system for tumour classification based on the shape of tumours. A database of benign and malignant tumour phantoms was created using dielectrically–representative tissue-mimicking material. Classification of benign and malignant tumour models of the experimental data was completed with Linear Discriminant Analysis, Quadratic Discriminant Analysis and Support Vector Machines classifiers.