Cluster analysis is a prime Pattern Recognition method used to categorize sample patterns in a population by means of forming different clusters by assigning cluster memberships to the sample patterns depending on the feature similarity relationship among different patterns. Patterns displaying dissimilar feature values are assigned different cluster memberships whereas patterns carrying similar feature values are placed into same cluster. Searching the relationship among horticultural data has become a major research area in Pattern Recognition. In this paper we have used the morphological features for describing the characteristics of Tomato leaves and fruits belonging to different classes. Morphological feature values are extracted from different tomato leaf and fruiting habit samples to analyze through K-Means and Two-step clustering techniques to segment leaf and fruit samples into separate clusters according to their species owing to categorize them. Our experimentation also compares and discusses about the importance of the features which are obtained through K-Means and Two-step Clustering technique, may be useful for leaf and fruit species categorization.