Morphological and morphometric traits were used to determine the relationship among Red (Auraki), Black (Duni), White (Fari), Brown (Idabari) and Brown-white (Idabari-fari) donkeys at different ages. A total of 700 donkeys were used for the study. The morphological data were collected on hair type, Coat colour, Skin type, Coat colour pattern, Tail shape, Eye colour and Head profile.Morphometric measures taken were body weight, head length, head width, ear length, neck length, neck circumference, shoulder width, height at withers, heart girth, Body length and Tail length. Data obtained were subjected to different statistical analysis to determine the distribution of phenotypic traits across classes based on morphological traits. ANOVA was used to determine the effect of various classes and morphological factors on morphometric indices; correlated relationship among morphometric traits for different classes, sex and strains were carried out; principal component analysis of morphometric traits, prediction equation for body weight based on morphometric measurements, discriminant analysis for the identification of donkeys with high discriminant powers and cluster analysis to classify donkeys into different genetic groups were carried out. Significant variations was observed between morphological and morphometric (P<0.01) traits. The results of the study showed that the short-smooth hair type (75%), brown coat colour (86%), thick skin type (75%), solid coat colour pattern (97%), pendent tail shape (58%), black eye colour (95%) and straight head profile (86%) were preponderant in the studied populations. The correlated relationships among morphometric measurements varied significantly (p<0.05, 0.01) with age in pooled data and within strains. Coefficients were very high across sexes within strains. Body weight was positively and negatively correlated with other measurements due to the effect of age. Principal component indices had higher loadings for head length (0.82), ear length (0.65), neck length (0.68), neck circumference (0.72), shoulderwidth (0.83), height at withers (0.80), heart girth (0.79) and body length (0.84) in PC1 across the strains. Sexual loadings indicated male having better prediction result when body volume and length components are considered while females are better evaluated using length and shape related measures. The BL (R2=97%), HW (R2=99%) and HG (R2=100%) predominated as the major single most important predictors of body weight in a stepwise regression across the sex and age categories. Brown-fari (Idabari-fari) donkey strain was highly discriminated against with high discriminating power and this could be classified as a distinct strain of donkey. Cluster analysis of qualitative traits of donkeys using UPMGA revealed high level of dilution of the genetic pool. It is therefore concluded that donkeys in Northwestern Nigeria can be characterized using their morphological and morphometric traits. However, for their distinct classification, into the four (4) existing strains, body weight, neck circumference and tail length cannot be used; rather a more appropriate yardstick is the use of skin type (36%) and tail shape (24%) which had high genetic distance estimate.